Text Generation
Transformers
PyTorch
English
gpt2
chat
palmyra
text-generation-inference
How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Writer/palmyra-20b-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Writer/palmyra-20b-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Use Docker
docker model run hf.co/Writer/palmyra-20b-chat
Quick Links

DEPRECATED MODEL NOTICE

Please note that this model is no longer maintained or supported by our team. We strongly advise against using it in production or for any critical applications.

Instead, we recommend using our latest and greatest models, which can be found at:

https://huggingface.co/collections/Writer/palmyra-writer-license-66476fa8156169f8720a2c89

==========================

Writer/palmyra-20b-chat


Usage


import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer

model_name = "Writer/palmyra-20b-chat"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
)

prompt = "What is the meaning of life?"

input_text = (
    "A chat between a curious user and an artificial intelligence assistant. "
    "The assistant gives helpful, detailed, and polite answers to the user's questions. "
    "USER: {prompt} "
    "ASSISTANT:"
)

model_inputs = tokenizer(input_text.format(prompt=prompt), return_tensors="pt").to(
    "cuda"
)

gen_conf = {
    "top_k": 20,
    "max_new_tokens": 2048,
    "temperature": 0.6,
    "do_sample": True,
    "eos_token_id": tokenizer.eos_token_id,
}

streamer = TextStreamer(tokenizer)
if "token_type_ids" in model_inputs:
    del model_inputs["token_type_ids"]

all_inputs = {**model_inputs, **gen_conf}
output = model.generate(**all_inputs, streamer=streamer)

print("-"*20)
print(output)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 38.97
ARC (25-shot) 43.52
HellaSwag (10-shot) 72.83
MMLU (5-shot) 35.18
TruthfulQA (0-shot) 43.17
Winogrande (5-shot) 66.46
GSM8K (5-shot) 3.94
DROP (3-shot) 7.7
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