DeepSeek-R1-Distill-Qwen-14B-GPTQ_4bit-128g/README.md

library_name, license, base_model, base_model_relation

library_name	license	base_model	base_model_relation
transformers	mit	deepseek-ai/DeepSeek-R1-Distill-Qwen-14B	quantized

GPTQ 4bit quantized version of DeepSeek-R1-Distill-Qwen-14B

Model Details

See details on the official page of the model: DeepSeek-R1-Distill-Qwen-14B

Quantized using GPTQModel using wikitext2 dataset with nsamples=256 and seqlen=1024. Quantization config:

bits=4,
group_size=128,
desc_act=False,
damp_percent=0.01,

Minimum VRAM required: ~11GB

How to use

Using transformers library with integrated GPTQ support:

from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

model_name = "avoroshilov/DeepSeek-R1-Distill-Qwen-14B-GPTQ_4bit-128g"

tokenizer = AutoTokenizer.from_pretrained(model_name)
quantized_model = AutoModelForCausalLM.from_pretrained(model_name, device_map='cuda')

chat = [{"role": "user", "content": "Why is grass green?"},]
question_tokens = tokenizer.apply_chat_template(chat, add_generation_prompt=True, return_tensors="pt").to(quantized_model.device)
answer_tokens = quantized_model.generate(question_tokens, generation_config=GenerationConfig(max_length=2048, ))[0]

print(tokenizer.decode(answer_tokens))