Instructions to use Davizig10jojo/BlazerNano-V2-Again with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Davizig10jojo/BlazerNano-V2-Again with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M

Use Docker

docker model run hf.co/Davizig10jojo/BlazerNano-V2-Again:Q4_K_M

LM Studio
Jan
Ollama
How to use Davizig10jojo/BlazerNano-V2-Again with Ollama:
```
ollama run hf.co/Davizig10jojo/BlazerNano-V2-Again:Q4_K_M
```

Unsloth Studio

How to use Davizig10jojo/BlazerNano-V2-Again with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Davizig10jojo/BlazerNano-V2-Again to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Davizig10jojo/BlazerNano-V2-Again to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Davizig10jojo/BlazerNano-V2-Again to start chatting

How to use Davizig10jojo/BlazerNano-V2-Again with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Davizig10jojo/BlazerNano-V2-Again:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Davizig10jojo/BlazerNano-V2-Again with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Davizig10jojo/BlazerNano-V2-Again:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Davizig10jojo/BlazerNano-V2-Again with Docker Model Runner:
```
docker model run hf.co/Davizig10jojo/BlazerNano-V2-Again:Q4_K_M
```

Lemonade

How to use Davizig10jojo/BlazerNano-V2-Again with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Davizig10jojo/BlazerNano-V2-Again:Q4_K_M

Run and chat with the model

lemonade run user.BlazerNano-V2-Again-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Davizig10jojo/BlazerNano-V2-Again with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Davizig10jojo/BlazerNano-V2-Again:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Davizig10jojo/BlazerNano-V2-Again:Q4_K_M

Run Hermes

hermes

Atomic Chat

language:

pt
en license: apache-2.0 base_model: Qwen/Qwen3-0.6B datasets:
Polygl0t/gigaverbo-v2-sft
Manusagents/GPT-5.5-Gemini-3.1-Pro-Grok-4-Claude-Fable-5-Mythos-5-Qwen-3.7-Max-and-more-Distillation-Dataset
Davizig10jojo/BlazerNano_V2 tags:
portuguese
fine-tuned
gguf
qwen
llama-cpp pipeline_tag: text-generation library_name: transformers inference: false

BlazerNano-V2-Again

Dataset Composition

50% Polygl0t/gigaverbo-v2-sft (PT-BR conversations)
30% Manusagents distillation dataset
20% Davizig10jojo/BlazerNano_V2

Training Details

Base Model: Qwen/Qwen3-0.6B
Method: LoRA (r=4, alpha=8)
GPUs: 2x T4 (Kaggle)
Batch Size: 1 per GPU (effective batch: 8)
Learning Rate: 0.0003
Epochs: 1

Quantized Versions

Format	File	Size
Q6_K	blazernano-v2-again-Q6_K.gguf	~350MB
Q4_K_M	blazernano-v2-again-Q4_K_M.gguf	~250MB

Usage with llama.cpp

huggingface-cli download Davizig10jojo/BlazerNano-V2-Again blazernano-v2-again-Q6_K.gguf --local-dir ./ --local-dir-use-symlinks False
./llama-cli -m blazernano-v2-again-Q6_K.gguf -p "Olá, como você está?" -n 256

Downloads last month: 73

Safetensors

Model size

0.8B params

Tensor type

BF16

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Model tree for Davizig10jojo/BlazerNano-V2-Again

Quantizations

1 model

Davizig10jojo
/

BlazerNano-V2-Again

BlazerNano-V2-Again

Dataset Composition

Training Details

Quantized Versions

Usage with llama.cpp

Model tree for Davizig10jojo/BlazerNano-V2-Again

Datasets used to train Davizig10jojo/BlazerNano-V2-Again