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LICENSE.md

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README.md

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+---
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+base_model: ibm-granite/granite-4.2-3b
+license: other
+license_name: webai-non-commercial-license-ver.-1.0
+license_link: https://huggingface.co/webAI-Official/TwIL-LM3-Pro/blob/main/LICENSE.md
+tags:
+  - granite
+  - granite-4.2
+  - formal-logic
+  - reasoning
+  - lora
+  - model-merging
+  - wise-ft
+  - reinforcement-learning
+  - grpo
+  - gguf
+---
+
+# TwIL-LM3-Pro
+
+A 3.66B reasoning model for **formal logic** tasks, built from
+[`ibm-granite/granite-4.2-3b`](https://huggingface.co/ibm-granite/granite-4.2-3b) through LoRA
+supervised fine-tuning, checkpoint fusion, WiSE-FT weight interpolation, and entropy-weighted
+GRPO reinforcement learning.
+
+It improves in-domain formal-logic performance by **+28% relative** over its base model
+(macro gate 0.431 → 0.554) **while holding held-out benchmark performance** — the 10-dataset
+macro is level with the base (0.7942 → 0.7901) and the 14-dataset macro improves slightly
+(0.7332 → 0.7425). On the same harness and sampled rows it reaches the highest Track A macro
+gate, strict-7 and six-lane average of any arm in the tables below for which each can be
+computed, including Qwen3-8B, Qwen3.5-4B, VibeThinker-3B and gpt-oss-120b (the 120B has no gate or strict-7
+value).
+
+![TwIL-LM3-Pro formal and general reasoning benchmarks against VibeThinker-3B, VibeThinker-webAI-trained, Qwen3.5-4B, Qwen3-8B, gpt-oss-120b, LFM2.5-8B-A1B and TwIL-LM3](benchmarks.png)
+
+*VibeThinker-webAI-trained is the public VibeThinker-3B after the same post-training pipeline (SLERP, d = 0.5, t = 0.5); see [Against the tuned VibeThinker-3B](#against-the-tuned-vibethinker-3b). It has no strict-7 or six-lane-average value, so those bars show n/a.*
+
+## Highlights
+
+* **Large in-domain gain on the same harness.** Macro gate 0.4313 → 0.5539 (+0.123) and strict-7
+  0.1821 → 0.2879 against its own base, measured on identical sampled rows. Both models are
+  heavily truncated at this budget (see [Limitations](#limitations-and-caveats)), and the base
+  more so, so the size of the gap is indicative rather than exact.
+* **Top of the Track A summary rows at 3.66B.** Macro gate 0.5539 against Qwen3-8B's 0.5336
+  (2.2x the parameters) and TwIL-LM3's 0.4218; strict-7 0.2879 against 0.2093 and 0.1971;
+  six-lane average 0.5389 against gpt-oss-120b's 0.5192. The gate lead over Qwen3-8B is 0.020 —
+  smaller than the sampling noise at n = 200 per lane — so read that one as parity, not a win.
+* **Strongest strict MCQ, and close to the best language-model fit.** `mcq_answer` strict accuracy
+  0.4100 (next best 0.1700), `lean_critic` 0.7950 (tied with Qwen3-8B and its own base), the
+  lowest `lm_corpus` perplexity of any arm (2.3130) and a `math_corpus` perplexity of 3.6983 that
+  only Qwen3.5-4B beats (3.5926). Perplexity is per token, so it is only loosely comparable across
+  tokenizers.
+* **Holds general capability.** Track B 10-dataset macro 0.7901 and 14-dataset macro 0.7425 —
+  ahead of LFM2.5-8B-A1B (0.7884 / 0.7378) at less than half its parameter count, and behind
+  Qwen3-8B (0.8493 / 0.7591) and gpt-oss-120b (0.8689 / 0.8086). It gains on BBH-logic
+  (0.9013 → 0.9540), MATH-500 (0.6567 → 0.7467) and MuSR (0.5922 → 0.6409), and gives back
+  GSM-Symbolic (0.8900 → 0.8267) and ARC (0.8933 → 0.8600).
+* **Against VibeThinker-3B, a reasoning model of similar size.** TwIL-LM3-Pro leads it on all
+  six Track A lanes — macro gate 0.5539 against 0.4118, strict-7 0.2879 against 0.2021 — but not
+  on Track B, where VibeThinker-3B is ahead on the 10-dataset macro (0.8097 against 0.7901) and
+  TwIL-LM3-Pro is ahead on the 14-dataset macro (0.7425 against 0.7262). That 14-dataset lead
+  comes entirely from BBH-logic (0.9540 against 0.6107); without that row TwIL-LM3-Pro trails.
+* **The pipeline is not tied to one model.** The same recipe was run on five base models. On
+  VibeThinker-3B it lifts the Track A macro gate from 0.374 to 0.508 (SLERP, the
+  *VibeThinker-webAI-trained* column) while the Track B 10-dataset macro moves from 0.815 to 0.802 — see
+  [One pipeline, several models](#one-pipeline-several-models).
+* **Structured formal output.** Tuned for the objects rather than the prose: FOL translation,
+  entailment labels, semantic parses, Lean statements and Lean proof critique.
+* **Runs anywhere.** 3.66B parameters in bf16 (6.82 GiB), with a Q4\_K\_M GGUF at 2.09 GiB for
+  CPU or 4 GB of VRAM.
+
+It is **not the most efficient**: it reasons at length. Track A generations average 1,902 tokens,
+against 564 for TwIL-LM3, and 24.2% of them hit the length cap. Its raw decode rate is high
+(21,169 tok/s †) but the long answers bring it to about 11 completed
+answers per second †, against 28.1 for TwIL-LM3 and 4.5 for Qwen3-8B. It is also not a general
+assistant — there is no safety or preference tuning here beyond what Granite 4.2 carries. See
+[Limitations](#limitations-and-caveats).
+
+## Model Details
+
+| Property                  | Value                                                                                          |
+| ------------------------- | ---------------------------------------------------------------------------------------------- |
+| Model ID                  | `webAI-Official/TwIL-LM3-Pro`                                                              |
+| Base model                | [`ibm-granite/granite-4.2-3b`](https://huggingface.co/ibm-granite/granite-4.2-3b)              |
+| Total parameters          | 3.66B (3,659,737,600)                                                                          |
+| Architecture              | Granite decoder-only dense transformer (`GraniteForCausalLM`); 40 layers, hidden size 2560, 40 attention heads / 8 KV heads |
+| Input / output            | Text / text                                                                                    |
+| Language                  | English                                                                                        |
+| Tokenizer vocabulary size | 100,352                                                                                        |
+| Context window            | 131,072 tokens                                                                                 |
+| Checkpoint precision      | bfloat16 (6.82 GiB), plus Q4\_0 / Q4\_K\_M / Q5\_K\_M / Q6\_K / Q8\_0 / F16 GGUF builds      |
+| Post-training             | LoRA SFT → checkpoint fusion → WiSE-FT (α = 0.15) → MGPO reinforcement learning (β = 0.02, step 2580) |
+| Reasoning format          | Emits a `<think>…</think>` block before the answer (default chat template)                     |
+| Evaluated decoding        | Greedy, 2048 new tokens (one retry at 4096), `max_seq_len` 8192                                |
+| Specialisation            | Formal logic: FOL translation, entailment, semantic parsing, Lean formalisation and critique   |
+| License                   | webAI Non-Commercial License ver. 1.0                                                          |
+
+The base model's 131,072-token context is carried through unchanged, but every score on this card
+was measured inside an 8,192-token window; longer contexts are inherited rather than validated
+here.
+
+## Results
+
+### Track A — in-domain formal logic
+
+TwIL-LM3-Pro, its base and VibeThinker-3B were run through the same harness, prompts, decoding
+settings and sampled rows described under [Evaluation protocol](#evaluation-protocol), and the
+other peer columns are the values already published for TwIL-LM3 on its card, produced by that
+same harness (see [Comparability](#limitations-and-caveats)). Throughput rows come from a
+dedicated decode-throughput protocol: `ans/s` is defined throughout as
+`tok/s ÷ mean generation length`, so it measures completed answers rather than raw decode rate.
+Cells marked † need the engine note below.
+
+| lane / metric | TwIL-LM3-Pro | Granite-4.2-3B base | VibeThinker-3B | VibeThinker-webAI-trained ★ | Qwen3.5-4B | TwIL-LM3 | Llama-3.2-3B | LFM2-2.6B | LFM2.5-8B-A1B | Qwen3-8B | gpt-oss-120b ‡ |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| lean_formalize token_f1 | 0.5092 | 0.2943 | 0.2087 | 0.527 | 0.4996 | 0.5869 | 0.3690 | 0.1321 | 0.4655 | 0.4022 | **0.6306** |
+| rule_induction derivation | 0.4195 | 0.2267 | 0.2038 | 0.227 | 0.5078 | 0.3192 | 0.0825 | 0.0615 | 0.1936 | 0.3680 | **0.6518** |
+| entailment_label accuracy | 0.6700 | 0.3000 | 0.5500 | 0.685 | 0.2400 | 0.5750 | 0.3300 | 0.4700 | 0.5400 | 0.5800 | **0.7750** |
+| mcq_answer accuracy | **0.4100** | 0.1200 | 0.1700 | — | 0.0000 | 0.1100 | 0.0000 | 0.0150 | 0.0750 | 0.0000 | 0.0700 |
+| semantic_parse token_f1 | 0.4295 | 0.3910 | 0.2422 | 0.392 | 0.3826 | **0.4416** | 0.3102 | 0.3665 | 0.3778 | 0.4257 | 0.4331 |
+| lean_critic accuracy | **0.7950** | **0.7950** | 0.6000 | 0.685 | 0.5400 | 0.6600 | 0.5300 | 0.5900 | 0.5500 | **0.7950** | 0.5550 |
+| lm_corpus perplexity ↓ | **2.3130** | 2.6334 | 16.9253 | 7.350 | 2.3916 | 2.8972 | 2.8478 | 4.3815 | 4.9472 | 2.5440 | 912.23 § |
+| math_corpus perplexity ↓ | 3.6983 | 4.4864 | 27.0318 | 7.000 | **3.5926** | 3.8229 | 4.7531 | 6.7472 | 8.3323 | 4.0083 | 1045.63 § |
+| average, 6 lanes | **0.5389** | 0.3545 | 0.3291 | — | 0.3617 | 0.4488 | 0.2703 | 0.2725 | 0.3670 | 0.4285 | 0.5192 |
+| **macro gate** | **0.5539** | 0.4313 | 0.4118 | 0.508 | 0.4466 | 0.4218 | 0.2925 | 0.3473 | 0.3757 | 0.5336 | — |
+| **strict-7** | **0.2879** | 0.1821 | 0.2021 | — | 0.1121 | 0.1971 | 0.1229 | 0.1579 | 0.1714 | 0.2093 | — |
+| macro_primary | **0.5875** | 0.4825 | 0.4637 | 0.579 | 0.4313 | 0.4475 | 0.3450 | 0.4188 | 0.4213 | 0.5750 | — |
+| tok/s | 21169 † | 21916 † | 28010 † | ≈28010 ★ † | 11097 † | 15880 | 16160 | 25230 | 22480 | 9420 | 3374 |
+| mean gen length | 1902 | 2951 | 2688 | — | 2486 | **564** | 696 | 2296 | 1830 | 2094 | 1005 |
+| **ans/s** | 11.1 † | 7.4 † | 10.4 † | — | 4.5 † | **28.1** | 23.2 | 10.9 | 12.0 | 4.5 | 3.4 |
+
+‡ **gpt-oss-120b** runs MXFP4 weights at tensor-parallel 2 — quantized and multi-GPU, so its
+throughput rows are not directly comparable to the single-GPU BF16 arms. Its `procedural` lane
+and the loose-match scorings were not collected, so the three summary rows below the six-lane
+average cannot be computed for it; that is what the — cells mean, not a zero.
+
+§ The 120B's perplexities are three orders of magnitude off every other arm because its response
+format and tokenizer make the corpus lanes score a different quantity. The number is reported
+for completeness but is not a comparable measurement, and is excluded from the bolding.
+
+★ **VibeThinker-webAI-trained** is the public VibeThinker-3B after the same post-training pipeline as
+TwIL-LM3-Pro, in its SLERP (d = 0.5, t = 0.5) configuration; it is not a checkpoint in this
+repository, and [the section below](#against-the-tuned-vibethinker-3b) explains how it differs
+from the base model. Its figures are taken from the internal family comparison tables, to three
+decimals, not from the per-lane reports behind the other columns, so its column is indicative
+rather than strictly paired with them. `macro gate` and `macro_primary` are the family tables'
+"macro primary (rule)" and "primary" columns, which have the same definitions as above. The
+family tables score `mcq_answer` with loose-match credit (0.695), so the strict MCQ row, the
+six-lane average and strict-7 — which all need strict scoring — are left as —. Its perplexities
+come from that separate run; the family table records the untuned VibeThinker-3B at 18.70 and
+21.80 there, against 16.93 and 27.03 in the columns above, so read them within their own source.
+Its `tok/s` is an **estimate, not a measurement**: merging changes weights but not architecture,
+parameter count or tokenizer, and the throughput protocol fixes the output at 512 generated tokens
+with EOS ignored, so the decode rate does not depend on what the weights say. It is therefore set
+equal to the 28,010 tok/s measured for the base VibeThinker-3B in the same session (a ≈ and both
+marks in the cell). Mean generation length, and so `ans/s`, does depend on the weights and was not
+measured, so those rows stay —.
+
+† **Throughput for TwIL-LM3-Pro, its base and VibeThinker-3B** was measured with the same
+dedicated protocol and prompt file as the peer columns (128 prompts × 512 generated tokens, EOS
+ignored, greedy, `gpu_memory_utilization` 0.45, idle GPU), run on a single H200 with vLLM
+0.19.1 in a later session, whereas the other columns are figures recorded earlier on vLLM 0.11.2. Engine effects
+are architecture-dependent: in the same session TwIL-LM3 re-measured at 15,248 tok/s
+against its published 15,880 (−4%), while VibeThinker-3B measured
+28,010 against a published 18,116 (+55%). The †
+cells are therefore comparable with each other, and only approximately with the older columns.
+The Qwen3.5-4B figure comes from the earlier throughput sweep, which ran the Qwen3.5 models on the
+newer engine (vLLM 0.19.1) according to its driver script, so it belongs with the † cells.
+TwIL-LM3-Pro's two runs gave 20,454 and 21,884 tok/s (the first paid for cold
+kernel caches) and the table shows their mean. `mean gen length` is measured directly under the
+shared evaluation protocol and is comparable across every column.
+
+**`average, 6 lanes`** is the plain mean of the six objective rows above it, each at whatever
+scoring that row reports (Lean F1, rule derivation, entailment, MCQ strict, semantic F1, critic).
+It is a coarser summary than the three that follow — it mixes token-F1 with accuracy — but it is
+the only summary row every arm here can be compared on, including the 120B.
+
+The next three rows aggregate more carefully. None of them include the perplexity lanes or the
+token-F1 scorings, which are not on a common 0–1 accuracy scale.
+
+**`macro gate`** is the headline metric and the one the training pipeline gates on. It is the
+equal-weight mean of five objectives: the four bounded classification lanes (`entailment_label`,
+`mcq_answer`, `procedural`, `lean_critic`) plus `rule_induction`, scored by its continuous
+derivation score. In the gate, `mcq_answer` and `procedural` are credited as
+`max(exact_match, loose_match)`: for free-text answer lanes, a response that is correct but
+differently formatted is a formatting artefact rather than a reasoning failure. This affects the
+aggregate only — the per-lane rows above stay strict. (TwIL-LM3-Pro's loose-match MCQ is
+0.6500 against the 0.4100 strict figure shown in the lane row.)
+
+**`macro_primary`** is the same mean over the four classification lanes alone, without
+`rule_induction`.
+
+**`strict-7`** is the mean of seven lanes scored under strict metrics only (`fol_translation`,
+`entailment_label`, `mcq_answer`, `semantic_parse` and `lean_formalize` exact match,
+`lean_critic` and `procedural` accuracy), with no loose-match credit anywhere. It is deliberately
+harsh — exact match on generative lanes is near zero for every arm — so it is useful for ranking
+models against each other but not as an absolute capability measure.
+
+TwIL-LM3-Pro leads all four summary rows that every arm with a computable value can be
+compared on. The clearest margins are over the arms at its own scale and above: 0.5539 against
+0.3757 on the gate for LFM2.5-8B-A1B, and 0.2879 against 0.1971 on strict-7 for TwIL-LM3. Against
+Qwen3-8B the gate gap is only 0.020, but strict-7 is 0.2879 against 0.2093 — a difference that
+does not depend on loose-match credit — and TwIL-LM3-Pro leads on MCQ (strict 0.4100 against
+0.0000), rule induction (0.4195 against 0.3680) and both perplexity lanes.
+
+VibeThinker-3B — WeiboAI's 3B reasoning model, with 37.1% of its Track A generations truncated
+— trails TwIL-LM3-Pro on every objective lane and every summary row: macro gate 0.4118 against
+0.5539, strict-7 0.2021 against 0.2879, six-lane average 0.3291 against 0.5389. The gap is widest
+on Lean formalisation (0.2087 against 0.5092) and narrowest on entailment (0.5500 against 0.6700).
+Its corpus perplexities (16.93 and 27.03) are not comparable with the Granite-tokenizer models',
+because perplexity is per token and its vocabulary is 151,936 against 100,352.
+
+Qwen3.5-4B, a 4B reasoning model, sits between
+the small models and TwIL-LM3-Pro: macro gate 0.4466 against 0.5539, strict-7 0.1121 against
+0.2879. It is ahead of TwIL-LM3-Pro on two rows only, `rule_induction` (0.5078 against 0.4195,
+second in the table after gpt-oss-120b) and `math_corpus` perplexity (3.5926 against 3.6983), and
+far behind on entailment (0.2400 against 0.6700) and strict MCQ (0.0000 against 0.4100).
+
+It does not lead every lane. gpt-oss-120b is clearly stronger on `rule_induction` (0.6518),
+entailment (0.7750) and Lean formalisation (0.6306), and TwIL-LM3 remains ahead on Lean
+formalisation (0.5869 against 0.5092) and semantic parsing (0.4416 against 0.4295). The two weak
+spots in absolute terms are `procedural` (strict 0.1200, loose 0.2350) and FOL translation
+(exact match 0.0100).
+
+### Track B — held-out benchmarks
+
+| dataset | TwIL-LM3-Pro | Granite-4.2-3B base | VibeThinker-3B | VibeThinker-webAI-trained ★ | Qwen3.5-4B | TwIL-LM3 | Llama-3.2-3B | LFM2-2.6B | LFM2.5-8B-A1B | Qwen3-8B | gpt-oss-120b ‡ |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| gsm8k | 0.9433 | 0.9533 | 0.9600 | 0.930 | 0.8633 | 0.8733 | 0.8300 | 0.8767 | 0.9133 | 0.9567 | **0.9767** |
+| svamp | 0.9500 | 0.9200 | 0.9367 | **0.953** | 0.8867 | 0.8500 | 0.8200 | 0.9000 | 0.9133 | 0.9400 | 0.9400 |
+| gsm_symbolic | 0.8267 | 0.8900 | 0.8167 | 0.883 | 0.7767 | 0.7567 | 0.8067 | **0.9767** | 0.9267 | 0.8133 | 0.8467 |
+| arc_cot | 0.8600 | 0.8933 | 0.9033 | 0.877 | 0.9267 | 0.8467 | 0.7967 | 0.8667 | 0.9033 | 0.9633 | **0.9667** |
+| logicbench | 0.7933 | 0.7733 | **0.8600** | 0.850 | 0.7200 | 0.7167 | 0.5733 | 0.6267 | 0.7200 | 0.8567 | 0.8533 |
+| strategyqa | 0.5967 | 0.6267 | 0.6467 | 0.647 | 0.6967 | 0.6500 | 0.6533 | 0.6433 | 0.6667 | 0.7400 | **0.7867** |
+| drop | 0.7367 | 0.7467 | 0.8467 | 0.790 | 0.6233 | 0.7467 | 0.6733 | 0.6900 | 0.6633 | **0.8833** | 0.8500 |
+| csqa | 0.7667 | 0.7733 | 0.7367 | 0.687 | 0.7767 | 0.7367 | 0.7500 | 0.7433 | 0.7700 | **0.8633** | 0.8367 |
+| musr | 0.6409 | 0.5922 | 0.5799 | 0.588 | 0.5696 | 0.4957 | 0.4932 | 0.4867 | 0.5703 | 0.6301 | **0.6852** |
+| mmlu_redux | 0.7867 | 0.7733 | 0.8100 | 0.813 | 0.8433 | 0.6667 | 0.6000 | 0.7133 | 0.8367 | 0.8500 | **0.9467** |
+| ifeval | 0.7500 | 0.7633 | 0.6633 | 0.557 | 0.2400 ¶¶¶ | 0.6433 | 0.7167 | 0.7300 | **0.8900** | 0.8400 | 0.7900 |
+| rudas_ood | 0.0437 ¶¶ | 0.0012 | 0.0056 | 0.000 | 0.0084 ¶¶¶ | 0.0365 | **0.0733** | 0.0017 | 0.0061 | 0.0468 | 0.0000 ¶ |
+| bbh_logic | 0.9540 | 0.9013 | 0.6107 | 0.829 | 0.9647 | 0.6633 | 0.5333 | 0.5713 | 0.7700 | 0.6367 | **0.9980** |
+| math500 | 0.7467 | 0.6567 | 0.7900 | 0.790 | 0.3600 ¶¶¶ | 0.6900 | 0.4233 | 0.7133 | 0.7800 | 0.6100 | **0.8433** |
+| **macro (10 CoT datasets)** | 0.7901 | 0.7942 | 0.8097 | 0.802 | 0.7683 | 0.7339 | 0.6997 | 0.7523 | 0.7884 | 0.8493 | **0.8689** |
+| **macro (all 14)** | 0.7425 | 0.7332 | 0.7262 | 0.728 | 0.6611 | 0.6694 | 0.6245 | 0.6814 | 0.7378 | 0.7591 | **0.8086** |
+| tok/s | 21169 † | 21916 † | 28010 † | ≈28010 ★ † | 11097 † | 15880 | 16160 | 25230 | 22480 | 9420 | 3374 |
+| mean gen length | ≈792 | ≈1282 | ≈1789 | — | ≈2787 | **482** | 510 | ≈796 | ≈1327 | ≈1931 | 801 |
+| **ans/s** | 26.7 † | 17.1 † | ≈15.7 † | — | ≈4.0 † | **32.9** | 31.7 | ≈31.7 | ≈16.9 | 4.9 | 4.2 |
+
+‡ MXFP4 weights, tensor-parallel 2 — quantized and multi-GPU, so not directly comparable to the
+single-GPU BF16 rows. ¶ 74% of its `rudas_ood` generations hit the length cap, so that cell is a
+truncation artefact rather than a measured score; excluding the row, its 13-dataset macro is
+0.8708. ¶¶ 93.7% of TwIL-LM3-Pro's `rudas_ood` generations also hit the length cap, so its
+cell is likewise a truncation artefact rather than a measurement of the model's ability.
+¶¶¶ Qwen3.5-4B hits the length cap on 67.3% of IFEval, 59.0% of MATH-500 and 99.3% of `rudas_ood`
+generations, so those three cells are truncation artefacts too.
+
+Lengths marked ≈ are derived from stored generations rather than read from the run. For
+TwIL-LM3-Pro, its base and VibeThinker-3B they were re-tokenized directly with each model's
+own tokenizer and
+averaged over the 14 datasets (MuSR counted once); the same method reproduces TwIL-LM3's measured
+482 to within 1%. The peer lengths marked ≈ use each model's characters-per-token ratio and are
+carried over from the TwIL-LM3 card. † `tok/s` is the same throughput figure as in Track A (see
+the engine note under that table) and `ans/s` divides it by the mean generation length shown. The
+VibeThinker-3B Track B rows come from the same external-baseline run and vLLM 0.11.2 engine as
+the other external peers.
+
+The honest summary of this table is that TwIL-LM3-Pro does not lead it. Larger models score
+higher, and gpt-oss-120b leads seven of the fourteen dataset rows. Three things are worth
+extracting anyway. First, it holds its own base on the held-out suite (10-dataset macro 0.7901
+against 0.7942, a difference well inside the sampling noise at n = 300 per dataset) while gaining
+in-domain, which is the point of the WiSE-FT stage. Second, the 14-dataset macro rises 0.0093
+over the base, driven by BBH-logic, MATH-500 and MuSR. Third, it edges LFM2.5-8B-A1B on both
+macros at less than half the parameters and leads every untuned arm on SVAMP (0.9500; the
+tuned VibeThinker-3B is at 0.953, one example out of 300 higher).
+
+VibeThinker-3B is the stronger held-out model on the 10-dataset macro (0.8097 against 0.7901). It
+is ahead of TwIL-LM3-Pro on seven datasets — GSM8K, ARC, LogicBench, StrategyQA, DROP,
+MMLU-Redux and MATH-500 — and behind on the other seven: SVAMP, GSM-Symbolic, CSQA, MuSR, IFEval,
+BBH-logic and `rudas_ood`. TwIL-LM3-Pro's 0.0163 lead on the 14-dataset macro comes entirely
+from BBH-logic (0.9540 against 0.6107): on the other thirteen datasets it averages 0.7263 against
+VibeThinker-3B's 0.7350. VibeThinker-3B also writes much longer answers on Track B (about 1,789
+tokens against 792).
+
+Track B here was run with the chat template's thinking mode **disabled** for TwIL-LM3-Pro and
+its base (the prompt ends in an empty `<think></think>`), as it was for TwIL-LM3, whereas Track A
+uses the default thinking mode. The Track B numbers therefore describe non-reasoning behaviour;
+they are not a measure of what a thinking-mode generation would score. Some Track B cells are also
+truncation-limited: MATH-500 hits the cap on 14.0% of rows, and SVAMP, GSM-Symbolic and
+MuSR-team sit slightly above the 2% cap-hit threshold (3.0%, 3.0% and 2.8%).
+
+### Against the tuned VibeThinker-3B
+
+The tables above use the public VibeThinker-3B checkpoint. The same post-training pipeline was also
+applied to it, and two of its tuned configurations are the closest same-scale comparisons to
+TwIL-LM3-Pro: WiSE-FT (λ = 0.50), and the SLERP merge (d = 0.5, t = 0.5) that appears in the
+tables and plot as **VibeThinker-webAI-trained**. These values come from the family comparison tables
+rather than from a per-lane raw report, so they are shown as a summary only:
+
+| model | macro gate | macro_primary | B10 | B14 | Track A truncation |
+|---|---:|---:|---:|---:|---:|
+| TwIL-LM3-Pro | **0.554** | **0.588** | 0.790 | **0.743** | 24.2% |
+| VibeThinker-webAI-trained (SLERP, d = 0.5, t = 0.5) | 0.508 | 0.579 | **0.802** | 0.728 | — |
+| VibeThinker-3B, WiSE-FT λ = 0.50 | 0.541 | **0.588** | **0.802** | 0.728 ◊ | 14.3% |
+
+◊ There is no B14 row for the λ = 0.50 configuration; the figure is the one recorded for the SLERP
+configuration in the row above. Truncation was not recorded for the SLERP configuration.
+
+Against VibeThinker-webAI-trained, TwIL-LM3-Pro is ahead on Track A (macro gate 0.554 against 0.508,
+`macro_primary` 0.588 against 0.579) and on the 14-dataset macro (0.743 against 0.728), and behind
+on the 10-dataset macro (0.790 against 0.802). Against the WiSE-FT λ = 0.50 configuration the two
+are effectively tied on Track A (gate 0.554 against 0.541, `macro_primary` equal at 0.588, both
+within sampling noise at n = 200), and the tuned VibeThinker-3B is ahead on the 10-dataset macro
+with a lower truncation rate. TwIL-LM3-Pro's edge is the 14-dataset macro, a gap that cannot be
+broken down per dataset from the summary values.
+
+#### How VibeThinker-webAI-trained differs from the base VibeThinker-3B
+
+**Base VibeThinker-3B** is WeiboAI's public checkpoint, unmodified, and is what the untuned columns
+in the tables above measure. **VibeThinker-webAI-trained** starts from those same weights and changes
+them in two ways:
+
+* **Formal-logic post-training.** A rank-64 LoRA is trained on the same synthetic formal-logic
+  corpus and Track A objectives used for TwIL-LM3-Pro (FOL translation, entailment, semantic
+  parsing, Lean formalisation and critique, procedural reasoning, rule induction), with multipath
+  distillation supplying the reasoning traces.
+* **Merging back toward the base.** The tuned weights are not used raw. They are merged into the
+  pretrained VibeThinker-3B weights with a DARE-SLERP merge (d = 0.5, t = 0.5, the configuration
+  labels used in the family tables), the same conservative interpolation step that protects
+  held-out capability in TwIL-LM3-Pro.
+
+The family tables record no reinforcement-learning (MGPO) run for VibeThinker-3B, so
+VibeThinker-webAI-trained reflects the supervised and merging stages only, whereas TwIL-LM3-Pro also has
+the MGPO stage. It is a reference point for the pipeline, not a checkpoint shipped in this
+repository.
+
+What that changes, on the family tables (one source, so the comparison is like for like):
+
+| metric | VibeThinker-3B (base) | VibeThinker-webAI-trained | change |
+|---|---:|---:|---:|
+| macro gate | 0.374 | 0.508 | +0.134 |
+| macro_primary | 0.444 | 0.579 | +0.135 |
+| entailment | 0.480 | 0.685 | +0.205 |
+| rule induction | 0.094 | 0.227 | +0.133 |
+| lean_critic | 0.580 | 0.685 | +0.105 |
+| `lm_corpus` perplexity ↓ | 18.70 | 7.35 | 2.5x lower |
+| `math_corpus` perplexity ↓ | 21.80 | 7.00 | 3.1x lower |
+| Track B, 10-dataset macro | 0.815 | 0.802 | −0.013 |
+| Track B, 14-dataset macro | 0.729 | 0.728 | −0.001 |
+
+The in-domain gain is large and the held-out change is inside sampling noise at n = 300 per
+dataset, which is the same trade the pipeline makes on Granite 4.2. Two per-dataset shifts are
+worth knowing: BBH-logic rises from 0.609 to 0.829, and IFEval falls from 0.660 to 0.557. Read the
+base column of this table against the family source only; the lane tables above record the same
+weights at a gate of 0.4118 from a separate run.
+
+### Checkpoint selection
+
+MGPO checkpoints were compared on both tracks, and step 2580 was published because it has the
+best (or tied-best) held-out score rather than the best in-domain gate:
+
+| checkpoint | macro gate | macro_primary | B10 | B14 |
+|---|---:|---:|---:|---:|
+| WiSE-FT α = 0.15 (RL initialiser) | 0.531 | 0.583 | — | — |
+| MGPO step 1200 | **0.569** | **0.598** | 0.7819 | 0.7362 |
+| MGPO step 2000 | 0.557 | 0.581 | 0.7882 | 0.7425 |
+| MGPO step 2200 | 0.567 | 0.583 | 0.7865 | 0.7406 |
+| **MGPO step 2580 (published)** | 0.554 | 0.588 | **0.7901** | **0.7425** |
+
+Track A gate here uses the same definition as the tables above (rule induction included).
+Step 1200 leads the gate by 0.015 and `macro_primary` by 0.010, but step 2580 has the highest
+10-dataset macro and ties step 2000 on the 14-dataset macro (0.7425 for both at four decimals),
+and the differences between the later checkpoints on Track A are within sampling noise at
+n = 200. The checkpoint-selection probe recorded for this release (100 prompts each from FOL
+translation, entailment and math MCQ, eight sampled completions per prompt at
+`temperature = 0.8`, `top_p = 0.95`) gave macro Pass@1 0.4071 and macro Pass@8 0.5567. That probe
+is a selection tool, not a benchmark claim.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "webAI-Official/TwIL-LM3-Pro"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+messages = [{"role": "user", "content":
+             "Does 'All dogs are mammals. Rex is a dog.' entail 'Rex is a mammal'? "
+             "Answer entailment, contradiction, or neutral."}]
+inputs = tok.apply_chat_template(
+    messages, add_generation_prompt=True,
+    return_tensors="pt", return_dict=True,
+).to(model.device)
+
+out = model.generate(**inputs, max_new_tokens=2048, do_sample=False)
+print(tok.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+`return_dict=True` matters on transformers 5.x, where `apply_chat_template` returns a
+`BatchEncoding` rather than a bare tensor; the above works on both 4.x and 5.x. Use a
+Transformers release with Granite 4.2 support.
+
+The reported numbers use **greedy decoding** (`do_sample=False`) and a **2048-token** generation
+budget. Note that the shipped `generation_config.json` enables sampling (`do_sample=true`,
+`temperature=1.0`, `top_p=0.95`), so `do_sample=False` must be passed explicitly to reproduce the
+evaluation. By default the chat template opens a `<think>` block, so the model reasons before it
+answers and a short generation budget truncates that reasoning and scores far worse. The template
+also accepts `enable_thinking=False` (empty think block, lower latency and lower quality on
+reasoning-heavy tasks) and `reasoning_effort="low"` through `chat_template_kwargs`.
+
+### GGUF / llama.cpp
+
+Quantized GGUF builds ship in this repository alongside the safetensors weights. The Granite
+architecture is supported by llama.cpp; the model uses a ChatML-style template with `<|im_end|>`
+as EOS, so run it in conversation mode (`-cnv`).
+
+| file | quant | size | bits/weight | notes |
+|---|---|---:|---:|---|
+| `TwIL-LM3-Pro-Q4_0.gguf` | Q4_0 | 1.98 GiB | 4.65 | for the Snapdragon Hexagon NPU via GenieX (see below) |
+| `TwIL-LM3-Pro-Q4_K_M.gguf` | Q4_K_M | 2.09 GiB | 4.91 | recommended default; runs on CPU or 4 GB of VRAM |
+| `TwIL-LM3-Pro-Q5_K_M.gguf` | Q5_K_M | 2.43 GiB | 5.71 | a little more headroom than Q4_K_M |
+| `TwIL-LM3-Pro-Q6_K.gguf` | Q6_K | 2.80 GiB | 6.57 | close to Q8_0 quality at about three-quarters the size |
+| `TwIL-LM3-Pro-Q8_0.gguf` | Q8_0 | 3.63 GiB | 8.51 | near-lossless, for quality-sensitive use |
+| `TwIL-LM3-Pro-F16.gguf` | F16 | 6.82 GiB | 16.01 | unquantized, for requantization or reference runs |
+
+```bash
+llama-cli -hf webAI-Official/TwIL-LM3-Pro:Q4_K_M -cnv --temp 0 -n 2048
+```
+
+Two things matter for reproducing the scores above under llama.cpp. Pass `--temp 0`, because the
+evaluation is greedy while the packaged sampling defaults are not. And leave the generation
+budget large — 2048 tokens or more — since the model emits a `<think>` block before answering
+and a short budget truncates it, which costs far more accuracy than the quantization does.
+
+F16 and Q8_0 were produced directly by `convert_hf_to_gguf.py` from the released bf16 weights; the
+K-quants (Q4_K_M, Q5_K_M, Q6_K) and Q4_0 were quantized from the F16 build with `llama-quantize`,
+without an importance matrix. F16 is not bit-identical to the released weights: bf16 and f16 carry the
+same 16 bits but trade exponent range against mantissa precision, so the conversion is a
+narrowing one, in practice negligible for inference.
+
+The published Track A and Track B numbers were measured on the **bf16** weights through vLLM, not
+on any of these GGUF builds, so expect small deviations — most likely at Q4_K_M and Q4_0 — that
+have not been quantified here.
+
+### Snapdragon NPU / GenieX
+
+The Q4_0 build is the one to use with [GenieX](https://github.com/qualcomm/GenieX) on Snapdragon
+devices (for example Snapdragon X Elite / X2 Elite), since Q4_0 has the best Hexagon NPU support in
+its llama.cpp runtime.
+
+```bash
+geniex infer webAI-Official/TwIL-LM3-Pro:Q4_0 --nctx 16384
+```
+
+```python
+from geniex import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained(
+    "webAI-Official/TwIL-LM3-Pro", precision="Q4_0", n_ctx=16384
+)
+```
+
+Set the context explicitly: GenieX defaults to a 4,096-token window for GGUF models, which is too
+short for this model's `<think>` blocks (1,902 tokens on average on Track A). The GGUF carries the
+full 131,072-token context, and on the NPU GenieX stores the KV cache in Q8_0, at about 42 KiB per
+token — roughly 0.7 GiB at 16k and 1.3 GiB at 32k on top of the weights. As above, scores were
+measured inside an 8,192-token window, and this build has not yet been benchmarked on device.
+
+## How it was built
+
+Five stages on top of the base model:
+
+1. **LoRA supervised fine-tuning** on a synthetic formal-logic corpus covering the Track A
+   objectives (first-order-logic translation, entailment labelling, semantic parsing, Lean
+   formalisation and critique, procedural reasoning, rule induction).
+2. **Multipath Distillation** to generate different reasoning traces from a given input prompt. 
+3. **Checkpoint fusion** — parameter-space averaging of intermediate SFT checkpoints, rather
+   than taking the final checkpoint.
+4. **WiSE-FT interpolation** along with **TIES** and **DARE-SLERP** merging toward the pretrained base. This conservative
+   interpolation is the direct reason held-out capability survives.
+5. **MGPO + Contrastive step loss** — entropy-weighted GRPO reinforcement learning against a programmatic verifier, with
+   partial credit for loose matches and token-F1 so that all-fail prompt groups still produce
+   gradient. Group size 16, learning rate 5e-6, sampling temperature 1.0, top-p 0.95,
+   γ = 3.0. The run was resumed at step 800 with β = 0.02 and trained through step 2580, and the
+   published checkpoint is **step 2580**. This release is the merged policy, not a LoRA adapter.
+
+## One pipeline, several models
+
+The recipe above is not specific to Granite 4.2. Each stage takes a base checkpoint, the shared
+formal-logic corpus and the programmatic verifier as inputs and hands the next stage a checkpoint
+of the same shape, so pointing the pipeline at a different base model does not change its
+structure. In the internal family comparison, the same rank-64 LoRA stage and the same merge
+families (WiSE-FT, DARE, SLERP) were run on five bases that differ in vocabulary, pretraining and
+size: an earlier Granite, SmolLM3, the TwIL base, VibeThinker-3B (151,936-token vocabulary) and
+Granite 4.2 (100,352). The MGPO stage was run on the two Granite bases.
+
+The one setting that is chosen per model is the merge coefficient, picked by a sweep over held-out
+performance: the best WiSE-FT weight is 0.15 for SmolLM3, 0.20 for Granite and the TwIL base, and
+0.50 for VibeThinker-3B, which needs to keep far more of the tuned weights than the others do.
+
+| base model | Track A macro gate (base → tuned) | Track B 10-dataset macro (base → tuned) |
+|---|---:|---:|
+| Granite | 0.309 → 0.454 | 0.777 → 0.786 |
+| SmolLM3 | 0.344 → 0.446 | 0.723 → 0.736 |
+| TwIL base | 0.423 → 0.435 | 0.729 → 0.716 |
+| VibeThinker-3B | 0.374 → 0.541 | 0.810 → 0.802 |
+| Granite 4.2 (this model)| 0.4313 → 0.5539 | 0.7942 → 0.7901 |
+
+Track A improves on every base, from +0.012 to +0.167, and the Track B change stays within ±0.013,
+inside sampling noise at n = 300 per dataset. The first four rows are from the family comparison
+tables and the last from the paired run described under [Results](#results), so the base gates
+are only comparable within a row. Reinforcement learning was applied only to the Granite bases
+here, so the other rows show what the supervised and merging stages achieve without it.
+
+## Limitations and caveats
+
+
+**Verbose by construction.** Track A generations average 1,902 tokens and Track B generations
+about 792, so cost per answer is substantially higher than the TwIL-LM family (564 and 482 tokens)
+even though quality per answer is higher on Track A.
+
+**Scope.** Tuned for formal logic. The Track B suite does not cover code generation or tool use
+(HumanEval, LiveCodeBench and BFCL were not run for this model or its base), so this release
+makes no claim about those. The weak absolute areas inside the specialisation are FOL translation
+(exact match 0.0100), `procedural` (strict 0.1200) and semantic parsing exact match (0.0000);
+`rule_induction` parses only 56.5% of outputs.
+
+**Comparability.** For Track A, TwIL-LM3-Pro, its base, VibeThinker-3B, Qwen3.5-4B, Qwen3-8B, LFM2-2.6B,
+LFM2.5-8B-A1B and Llama-3.2-3B were checked to share the same sampled-row manifest and dataset
+hash, seed and decoding; the TwIL-LM3 and gpt-oss-120b values are carried over from the TwIL-LM3
+card, which describes the same harness. For Track B, the arms checked (including VibeThinker-3B
+and Qwen3.5-4B, on all 18 tasks) share the same sampled rows and decoding, but the serving engine differs between
+arms (vLLM 0.19.1 for TwIL-LM3-Pro, its base, Qwen3.5-4B, Qwen3-8B and LFM2.5-8B-A1B; vLLM 0.11.2 for
+TwIL-LM3, Llama-3.2-3B and VibeThinker-3B), and the engine version is part of the protocol hash.
+The VibeThinker-webAI-trained column (★) comes from the internal family comparison tables and is not
+covered by the manifest checks described here. Throughput has its own, separate engine caveat (see the † note under the Track A table). With
+n = 200 per lane on Track A and n = 300 per dataset on Track B, differences of two to three points
+are within sampling noise.
+
+## Evaluation protocol
+
+- Track A: `n = 200` per objective, greedy (`temperature = 0`), `max_new_tokens = 2048`, one
+  retry at 4096 for truncated rows, `max_seq_len = 8192`, seed 42, default (thinking-enabled)
+  chat template.
+- Track B: 300 examples per task, greedy, `max_gen_toks = 4096`, `max_model_len = 8192`,
+  `repetition_penalty = 1.0`, chat template applied with thinking disabled, vLLM backend.
+- Both tracks use the same protocol for the model and its base, in a paired run over identical
+  sampled rows.
+- Throughput: 128 prompts drawn from a fixed Track A prompt file, 512 generated tokens each with
+  EOS ignored, greedy, vLLM `gpu_memory_utilization` 0.45, `max_model_len` 4096, on an otherwise
+  idle GPU (a single H200 for the TwIL-LM3-Pro, base and VibeThinker-3B runs); the reported rate is generated tokens over decode time, excluding engine start-up and
+  compilation.
+
+`repetition_penalty = 1.0` is load-bearing. A 1.1 penalty produced apparent 20-point swings on
+Track B that were pure decoding artefact; the decoding kwargs are hashed into the protocol
+identity so a mismatched runner fails loudly instead of quietly producing a different number.
+
+## Relationship to TwIL-LM
+
+TwIL-LM3-Pro applies the same post-training pipeline as the
+[TwIL-LM3](https://huggingface.co/webAI-Official/TwIL-LM3) and TwIL-LM family — LoRA SFT,
+checkpoint fusion, WiSE-FT and MGPO — to a different base, IBM's Granite 4.2 3B, instead of
+SmolLM3 or SmolLM2 with some additional mechanisms. Compared with TwIL-LM3 it is a stronger in-domain model (macro gate 0.5539
+against 0.4218) and a stronger held-out one (10-dataset macro 0.7901 against 0.7339), at the
+price of much longer generations and a much higher truncation rate. Like the TwIL-LM models, it
+ships as a full merged model on `main`, loaded directly with `AutoModelForCausalLM`.
+
+## License and attribution
+
+Released under the **webAI Non-Commercial License ver. 1.0** — see `LICENSE.md` in this
+repository.
+
+The base model, [`ibm-granite/granite-4.2-3b`](https://huggingface.co/ibm-granite/granite-4.2-3b),
+is Copyright IBM Corporation and is distributed under the Apache License 2.0; its licence text is
+retained as `apache-2.0-LICENSE.txt` and all credit for the base model goes to IBM. Apache 2.0
+permits distributing derivative works under different terms provided attribution is preserved,
+which is what the pair of licence files in this repository does.

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apache-2.0-LICENSE.txt

@@ -0,0 +1,202 @@
+
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+
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+   limitations under the License.

chat_template.jinja

@@ -0,0 +1,184 @@
+{%- macro tool_to_json(tool) -%}
+    {%- set ns_tool = namespace(first=true) -%}
+    {{- '{' -}}
+    {%- for k, v in tool.items() -%}
+        {%- if k != 'defer_loading' and k != 'strict' -%}
+            {%- if not ns_tool.first -%}{{- ', ' -}}{%- endif -%}
+            {%- set ns_tool.first = false -%}
+            {{- '"' ~ k ~ '": ' ~ (v | tojson(ensure_ascii=False)) -}}
+        {%- endif -%}
+    {%- endfor -%}
+    {{- '}' -}}
+{%- endmacro -%}
+{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %}
+{%- set low_effort = low_effort if low_effort is defined else False %}
+{%- if reasoning_effort is defined and reasoning_effort is not none %}
+    {%- set low_effort = reasoning_effort == "low" %}
+{%- endif %}
+{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %}
+
+{%- set ns = namespace(last_user_idx = -1) %}
+{%- set loop_messages = messages %}
+{%- for m in loop_messages %}
+  {%- if m["role"] == "user" %}
+    {%- set ns.last_user_idx = loop.index0 %}
+  {%- endif %}
+{%- endfor %}
+
+{%- if messages[0]["role"] == "system" %}
+    {%- set system_message = messages[0]["content"] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = [] %}
+{%- endif %}
+{# Recompute last_user_idx relative to loop_messages after handling system #}
+{%- set ns = namespace(last_user_idx = -1) %}
+{%- for m in loop_messages %}
+  {%- if m["role"] == "user" %}
+    {%- set ns.last_user_idx = loop.index0 %}
+  {%- endif %}
+{%- endfor %}
+{%- if system_message is defined %}
+    {{- "<|im_start|>system\n" + system_message }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- "<|im_start|>system\n" }}
+    {%- endif %}
+{%- endif %}
+{%- if tools is iterable and tools | length > 0 %}
+    {%- if system_message is defined and system_message | length > 0 %}
+        {{- "\n\n" }}
+    {%- endif %}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n" }}
+    {{- "<tools>" }}
+    {%- for tool in tools %}
+        {%- if tool.function is defined %}
+            {%- set tool = tool.function %}
+        {%- endif %}
+        {%- if tool.defer_loading is not defined or not tool.defer_loading %}
+            {{- '\n' ~ tool_to_json(tool) }}
+        {%- endif %}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+{%- endif %}
+
+
+{%- if system_message is defined %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+
+{%- for message in loop_messages %}
+    {%- if message.role == "assistant" %}
+        {# Add reasoning content in to content field for unified processing below. #}
+        {%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %}
+            {%- set content = "<think>\n" ~ message.reasoning_content ~ "\n</think>\n" ~ (message.content | default('', true)) %}
+        {%- else %}
+            {%- set content = message.content | default('', true) %}
+            {%- if content is string -%}
+                {# Allow downstream logic to to take care of broken thought, only handle coherent reasoning here. #}
+                {%- if '<think>' not in content and '</think>' not in content -%}
+                    {%- set content = "<think></think>" ~ content -%}
+                {%- endif -%}
+            {%- else -%}
+                {%- set content = content -%}
+            {%- endif -%}
+        {%- endif %}
+        {%- if message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}
+            {# Assistant message has tool calls. #}
+            {{- '<|im_start|>assistant\n' }}
+                {%- set include_content = not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}
+                {%- if content is string and content | trim | length > 0 %}
+                    {%- if include_content %}
+                        {{- (content | trim) ~ '\n' -}}
+                    {%- else %}
+                        {%- set c = (content | string) %}
+                        {%- if '</think>' in c %}
+                            {# Keep only content after the last closing think. Also generation prompt causes this. #}
+                            {%- set c = c.split('</think>')[-1] %}
+                        {%- elif '<think>' in c %}
+                            {# If <think> was opened but never closed, drop the trailing think segment #}
+                            {%- set c = c.split('<think>')[0] %}
+                        {%- endif %}
+                        {%- set c = "<think></think>" ~ c | trim %}
+                        {%- if c | length > 0 %}
+                            {{- c ~ '\n' -}}
+                        {%- endif %}
+                    {%- endif %}
+                {%- else %}
+                    {{- "<think></think>" -}}
+                {%- endif %}
+                {%- for tool_call in message.tool_calls %}
+                    {%- if tool_call.function is defined %}
+                        {%- set tool_call = tool_call.function %}
+                    {%- endif %}
+                    {{- '<tool_call>\n<function=' ~ tool_call.name ~ '>\n' -}}
+                        {%- if tool_call.arguments is defined %}
+                            {%- for args_name, args_value in tool_call.arguments|items %}
+                                {{- '<parameter=' ~ args_name ~ '>\n' -}}
+                                    {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                                {{- args_value ~ '\n</parameter>\n' -}}
+                            {%- endfor %}
+                        {%- endif %}
+                    {{- '</function>\n</tool_call>\n' -}}
+                {%- endfor %}
+                {{- '<|im_end|>\n' }}
+        {%- else %}
+            {# Assistant message doesn't have tool calls. #}
+            {%- if not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}
+                {{- '<|im_start|>assistant\n' ~ (content | default('', true) | string | trim) ~ '<|im_end|>\n' }}
+            {%- else %}
+                {%- set c = (content | default('', true) | string) %}
+                {%- if '<think>' in c and '</think>' in c %}
+                    {%- set c = "<think></think>" ~ c.split('</think>')[-1] %}
+                {%- endif %}
+                {%- set c = c | trim %}
+                {%- if c | length > 0 %}
+                    {{- '<|im_start|>assistant\n' ~ c ~ '<|im_end|>\n' }}
+                {%- else %}
+                    {{- '<|im_start|>assistant\n<|im_end|>\n' }}
+                {%- endif %}
+            {%- endif %}
+        {%- endif %}
+    {%- elif message.role == "user" or message.role == "system" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- set content = message.content | string %}
+        {%- if message.role == "user" and loop.index0 == ns.last_user_idx and low_effort %}
+            {{- content + '\n\n{reasoning effort: low}' }}
+        {%- else %}
+            {{- content }}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user\n' }}
+        {%- endif %}
+        {{- '<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>\n' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endfor %}
+
+{%- if add_generation_prompt %}
+    {%- if enable_thinking %}
+        {{- '<|im_start|>assistant\n<think>\n' }}
+    {%- else %}
+        {{- '<|im_start|>assistant\n<think></think>' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,34 @@
+{
+  "architectures": [
+    "GraniteForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attention_multiplier": 0.015625,
+  "bos_token_id": 100283,
+  "dtype": "bfloat16",
+  "embedding_multiplier": 1.0,
+  "eos_token_id": 100257,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.1,
+  "intermediate_size": 8192,
+  "logits_scaling": 1.0,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "granite",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "pad_token_id": 100257,
+  "residual_multiplier": 1.0,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 10000000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.15.0",
+  "use_cache": false,
+  "vocab_size": 100352
+}

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 100283,
+  "do_sample": true,
+  "eos_token_id": [
+    100257
+  ],
+  "pad_token_id": 100257,
+  "temperature": 1.0,
+  "top_p": 0.95,
+  "transformers_version": "5.15.0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:217bd76d3f59eff93f7d71119788f4d371f19c0a9899af2822e9162b74b13954
+size 7319517272

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,784 @@
+{
+  "add_prefix_space": false,
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|im_end|>",
+  "padding_side": "left",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "<unk>",
+  "added_tokens_decoder": {
+    "100256": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100257": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100258": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100259": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100260": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100261": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100262": {
+      "content": "<|filename|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100263": {
+      "content": "<|reponame|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100264": {
+      "content": "<|unused_11|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100265": {
+      "content": "<|unused_12|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100266": {
+      "content": "<|unused_13|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100267": {
+      "content": "<|unused_14|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100268": {
+      "content": "<|unused_15|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100269": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100270": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100271": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100272": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100273": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100274": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100275": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100276": {
+      "content": "[INST]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100277": {
+      "content": "[/INST]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100278": {
+      "content": "[AVAILABLE_TOOLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100279": {
+      "content": "[/AVAILABLE_TOOLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100280": {
+      "content": "[TOOL_RESULTS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100281": {
+      "content": "[/TOOL_RESULTS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100282": {
+      "content": "[TOOL_CALLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100283": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100284": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100285": {
+      "content": "<|unused_16|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100286": {
+      "content": "<|unused_17|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100287": {
+      "content": "<|unused_18|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100288": {
+      "content": "<|unused_19|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100289": {
+      "content": "<|unused_20|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100290": {
+      "content": "<|unused_21|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100291": {
+      "content": "<|unused_22|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100292": {
+      "content": "<|unused_23|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100293": {
+      "content": "<|unused_24|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100294": {
+      "content": "<|unused_25|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100295": {
+      "content": "<|unused_26|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100296": {
+      "content": "<|unused_27|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100297": {
+      "content": "<|unused_28|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100298": {
+      "content": "<|unused_29|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100299": {
+      "content": "<|unused_30|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100300": {
+      "content": "<|unused_31|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100301": {
+      "content": "<|unused_32|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100302": {
+      "content": "<|unused_33|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100303": {
+      "content": "<|unused_34|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100304": {
+      "content": "<|unused_35|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100305": {
+      "content": "<|unused_36|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100306": {
+      "content": "<|unused_37|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100307": {
+      "content": "<|unused_38|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100308": {
+      "content": "<|unused_39|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100309": {
+      "content": "<|unused_40|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100310": {
+      "content": "<|unused_41|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100311": {
+      "content": "<|unused_42|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100312": {
+      "content": "<|unused_43|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100313": {
+      "content": "<|unused_44|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100314": {
+      "content": "<|unused_45|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100315": {
+      "content": "<|unused_46|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100316": {
+      "content": "<|unused_47|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100317": {
+      "content": "<|unused_48|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100318": {
+      "content": "<|unused_49|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100319": {
+      "content": "<|unused_50|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100320": {
+      "content": "<|unused_51|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100321": {
+      "content": "<|unused_52|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100322": {
+      "content": "<|unused_53|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100323": {
+      "content": "<|unused_54|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100324": {
+      "content": "<|unused_55|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100325": {
+      "content": "<|unused_56|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100326": {
+      "content": "<|unused_57|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100327": {
+      "content": "<|unused_58|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100328": {
+      "content": "<|unused_59|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100329": {
+      "content": "<|unused_60|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100330": {
+      "content": "<|unused_61|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100331": {
+      "content": "<|unused_62|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100332": {
+      "content": "<|unused_63|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100333": {
+      "content": "<|unused_64|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100334": {
+      "content": "<|unused_65|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100335": {
+      "content": "<|unused_66|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100336": {
+      "content": "<|unused_67|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100337": {
+      "content": "<|unused_68|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100338": {
+      "content": "<|unused_69|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100339": {
+      "content": "<|unused_70|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100340": {
+      "content": "<|unused_71|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100341": {
+      "content": "<|unused_72|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100342": {
+      "content": "<|unused_73|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100343": {
+      "content": "<|unused_74|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100344": {
+      "content": "<|unused_75|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100345": {
+      "content": "<|unused_76|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100346": {
+      "content": "<|unused_77|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100347": {
+      "content": "<|unused_78|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100348": {
+      "content": "<|unused_79|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100349": {
+      "content": "<|unused_80|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100350": {
+      "content": "<|unused_81|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100351": {
+      "content": "<|unused_82|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "chat_template": "{%- macro tool_to_json(tool) -%}\n    {%- set ns_tool = namespace(first=true) -%}\n    {{- '{' -}}\n    {%- for k, v in tool.items() -%}\n        {%- if k != 'defer_loading' and k != 'strict' -%}\n            {%- if not ns_tool.first -%}{{- ', ' -}}{%- endif -%}\n            {%- set ns_tool.first = false -%}\n            {{- '\"' ~ k ~ '\": ' ~ (v | tojson(ensure_ascii=False)) -}}\n        {%- endif -%}\n    {%- endfor -%}\n    {{- '}' -}}\n{%- endmacro -%}\n{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %}\n{%- set low_effort = low_effort if low_effort is defined else False %}\n{%- if reasoning_effort is defined and reasoning_effort is not none %}\n    {%- set low_effort = reasoning_effort == \"low\" %}\n{%- endif %}\n{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %}\n\n{%- set ns = namespace(last_user_idx = -1) %}\n{%- set loop_messages = messages %}\n{%- for m in loop_messages %}\n  {%- if m[\"role\"] == \"user\" %}\n    {%- set ns.last_user_idx = loop.index0 %}\n  {%- endif %}\n{%- endfor %}\n\n{%- if messages[0][\"role\"] == \"system\" %}\n    {%- set system_message = messages[0][\"content\"] %}\n    {%- set loop_messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n    {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = [] %}\n{%- endif %}\n{# Recompute last_user_idx relative to loop_messages after handling system #}\n{%- set ns = namespace(last_user_idx = -1) %}\n{%- for m in loop_messages %}\n  {%- if m[\"role\"] == \"user\" %}\n    {%- set ns.last_user_idx = loop.index0 %}\n  {%- endif %}\n{%- endfor %}\n{%- if system_message is defined %}\n    {{- \"<|im_start|>system\\n\" + system_message }}\n{%- else %}\n    {%- if tools is iterable and tools | length > 0 %}\n        {{- \"<|im_start|>system\\n\" }}\n    {%- endif %}\n{%- endif %}\n{%- if tools is iterable and tools | length > 0 %}\n    {%- if system_message is defined and system_message | length > 0 %}\n        {{- \"\\n\\n\" }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou have access to the following functions:\\n\\n\" }}\n    {{- \"<tools>\" }}\n    {%- for tool in tools %}\n        {%- if tool.function is defined %}\n            {%- set tool = tool.function %}\n        {%- endif %}\n        {%- if tool.defer_loading is not defined or not tool.defer_loading %}\n            {{- '\\n' ~ tool_to_json(tool) }}\n        {%- endif %}\n    {%- endfor %}\n    {{- \"\\n</tools>\" }}\n\n    {{- '\\n\\nIf you choose to call a function ONLY reply in the following format with NO suffix:\\n\\n<tool_call>\\n<function=example_function_name>\\n<parameter=example_parameter_1>\\nvalue_1\\n</parameter>\\n<parameter=example_parameter_2>\\nThis is the value for the second parameter\\nthat can span\\nmultiple lines\\n</parameter>\\n</function>\\n</tool_call>\\n\\n<IMPORTANT>\\nReminder:\\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\\n- Required parameters MUST be specified\\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\\n</IMPORTANT>' }}\n{%- endif %}\n\n\n{%- if system_message is defined %}\n    {{- '<|im_end|>\\n' }}\n{%- else %}\n    {%- if tools is iterable and tools | length > 0 %}\n        {{- '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n\n{%- for message in loop_messages %}\n    {%- if message.role == \"assistant\" %}\n        {# Add reasoning content in to content field for unified processing below. #}\n        {%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %}\n            {%- set content = \"<think>\\n\" ~ message.reasoning_content ~ \"\\n</think>\\n\" ~ (message.content | default('', true)) %}\n        {%- else %}\n            {%- set content = message.content | default('', true) %}\n            {%- if content is string -%}\n                {# Allow downstream logic to to take care of broken thought, only handle coherent reasoning here. #}\n                {%- if '<think>' not in content and '</think>' not in content -%}\n                    {%- set content = \"<think></think>\" ~ content -%}\n                {%- endif -%}\n            {%- else -%}\n                {%- set content = content -%}\n            {%- endif -%}\n        {%- endif %}\n        {%- if message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}\n            {# Assistant message has tool calls. #}\n            {{- '<|im_start|>assistant\\n' }}\n                {%- set include_content = not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}\n                {%- if content is string and content | trim | length > 0 %}\n                    {%- if include_content %}\n                        {{- (content | trim) ~ '\\n' -}}\n                    {%- else %}\n                        {%- set c = (content | string) %}\n                        {%- if '</think>' in c %}\n                            {# Keep only content after the last closing think. Also generation prompt causes this. #}\n                            {%- set c = c.split('</think>')[-1] %}\n                        {%- elif '<think>' in c %}\n                            {# If <think> was opened but never closed, drop the trailing think segment #}\n                            {%- set c = c.split('<think>')[0] %}\n                        {%- endif %}\n                        {%- set c = \"<think></think>\" ~ c | trim %}\n                        {%- if c | length > 0 %}\n                            {{- c ~ '\\n' -}}\n                        {%- endif %}\n                    {%- endif %}\n                {%- else %}\n                    {{- \"<think></think>\" -}}\n                {%- endif %}\n                {%- for tool_call in message.tool_calls %}\n                    {%- if tool_call.function is defined %}\n                        {%- set tool_call = tool_call.function %}\n                    {%- endif %}\n                    {{- '<tool_call>\\n<function=' ~ tool_call.name ~ '>\\n' -}}\n                        {%- if tool_call.arguments is defined %}\n                            {%- for args_name, args_value in tool_call.arguments|items %}\n                                {{- '<parameter=' ~ args_name ~ '>\\n' -}}\n                                    {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}\n                                {{- args_value ~ '\\n</parameter>\\n' -}}\n                            {%- endfor %}\n                        {%- endif %}\n                    {{- '</function>\\n</tool_call>\\n' -}}\n                {%- endfor %}\n                {{- '<|im_end|>\\n' }}\n        {%- else %}\n            {# Assistant message doesn't have tool calls. #}\n            {%- if not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}\n                {{- '<|im_start|>assistant\\n' ~ (content | default('', true) | string | trim) ~ '<|im_end|>\\n' }}\n            {%- else %}\n                {%- set c = (content | default('', true) | string) %}\n                {%- if '<think>' in c and '</think>' in c %}\n                    {%- set c = \"<think></think>\" ~ c.split('</think>')[-1] %}\n                {%- endif %}\n                {%- set c = c | trim %}\n                {%- if c | length > 0 %}\n                    {{- '<|im_start|>assistant\\n' ~ c ~ '<|im_end|>\\n' }}\n                {%- else %}\n                    {{- '<|im_start|>assistant\\n<|im_end|>\\n' }}\n                {%- endif %}\n            {%- endif %}\n        {%- endif %}\n    {%- elif message.role == \"user\" or message.role == \"system\" %}\n        {{- '<|im_start|>' + message.role + '\\n' }}\n        {%- set content = message.content | string %}\n        {%- if message.role == \"user\" and loop.index0 == ns.last_user_idx and low_effort %}\n            {{- content + '\\n\\n{reasoning effort: low}' }}\n        {%- else %}\n            {{- content }}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.previtem and loop.previtem.role != \"tool\" %}\n            {{- '<|im_start|>user\\n' }}\n        {%- endif %}\n        {{- '<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>\\n' }}\n        {%- if not loop.last and loop.nextitem.role != \"tool\" %}\n            {{- '<|im_end|>\\n' }}\n        {%- elif loop.last %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- else %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endfor %}\n\n{%- if add_generation_prompt %}\n    {%- if enable_thinking %}\n        {{- '<|im_start|>assistant\\n<think>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>assistant\\n<think></think>' }}\n    {%- endif %}\n{%- endif %}\n"
+}