DeepSeek-R1-Distill-Llama-8B-quantized.w4a16

DeepSeek-R1-Distill-Llama-8B-quantized.w4a16

Model Overview

• Model Architecture: LlamaForCausalLM
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: INT4
• Release Date: 2/1/2025
• Version: 1.0
• Model Developers: Neural Magic

Quantized version of DeepSeek-R1-Distill-Llama-8B.

Model Optimizations

This model was obtained by quantizing the weights of DeepSeek-R1-Distill-Llama-8B to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.

Only the weights of the linear operators within transformers blocks are quantized. Weights are quantized using asymmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization, as implemented in the llm-compressor library.

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

number_gpus = 1
model_name = "Saktsant/DeepSeek-R1-Distill-Llama-8B-quantized.w4a16"

tokenizer = AutoTokenizer.from_pretrained(model_name)
sampling_params = SamplingParams(temperature=0.6, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
llm = LLM(model=model_name, tensor_parallel_size=number_gpus, trust_remote_code=True)

messages_list = [
    [{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
]

prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]

outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)

generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)

vLLM also supports OpenAI-compatible serving. See the documentation for more details.

Creation

from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.awq import AWQModifier
from llmcompressor import oneshot
from datasets import load_dataset

model_stub = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
model_name = model_stub.split("/")[-1]
num_samples = 512
max_seq_len = 2048
tokenizer = AutoTokenizer.from_pretrained(model_stub)
model = AutoModelForCausalLM.from_pretrained(
    model_stub,
    device_map="auto",
    torch_dtype="auto",
)


def preprocess_fn(example):
    return {
        "text": tokenizer.apply_chat_template(
            example["messages"], add_generation_prompt=False, tokenize=False
        )
    }


ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
ds = ds.map(preprocess_fn)

recipe = [
    AWQModifier(ignore=["lm_head"], scheme="W4A16_ASYM", targets=["Linear"]),
]

oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=max_seq_len,
    num_calibration_samples=num_samples,
)

save_path = model_name + "-quantized.w4a16"
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)