Deepseek R1 Distill Llama 70B FP8 Dynamic

🚀 DeepSeek-R1-Distill-Llama-70B-FP8-dynamic

A quantized version of DeepSeek-R1-Distill-Llama-70B, optimized for reduced disk size and GPU memory requirements.

🚀 Quick Start

This is a quantized version of DeepSeek-R1-Distill-Llama-70B. It can be deployed efficiently using the vLLM backend.

✨ Features

• Model Architecture: LlamaForCausalLM, taking text as input and outputting text.
• Model Optimizations:
  • Weight quantization: FP8
  • Activation quantization: FP8
• Release Date: 2/1/2025
• Version: 1.0
• Model Developers: Neural Magic

This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.

📦 Installation

No specific installation steps are provided in the original README. However, you can use the model with vLLM as shown in the usage examples.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

number_gpus = 2
model_name = "neuralmagic/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic"

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)

Advanced Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.compression.helpers import calculate_offload_device_map
import os

# Load model
model_stub = "deepseek-ai/DeepSeek-R1-Distill-Llama-70B"
model_name = model_stub.split("/")[-1]

device_map = calculate_offload_device_map(
    model_stub,
    reserve_for_hessians=True,
    num_gpus=2,
    torch_dtype="auto",
)

model = AutoModelForCausalLM.from_pretrained(
    model_stub,
    device_map=device_map,
    torch_dtype="auto",
)

tokenizer = AutoTokenizer.from_pretrained(model_stub)

# Configure the quantization algorithm and scheme
recipe = QuantizationModifier(
    targets="Linear",
    scheme="FP8_DYNAMIC",
    ignore=["lm_head"],
)

# Apply quantization
oneshot(
    model=model,
    recipe=recipe,
)

# Save to disk in compressed-tensors format
save_path = model_name + "-FP8-dynamic"
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
print(f"Model and tokenizer saved to: {save_path}")

📚 Documentation

Evaluation

The model was evaluated on OpenLLM Leaderboard V1 and V2, using the following commands:

OpenLLM Leaderboard V1:

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True \
  --tasks openllm \
  --write_out \
  --batch_size auto \
  --output_path output_dir \
  --show_config

OpenLLM Leaderboard V2:

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True \
  --apply_chat_template \
  --fewshot_as_multiturn \
  --tasks leaderboard \
  --write_out \
  --batch_size auto \
  --output_path output_dir \
  --show_config

Accuracy

Inference Performance

This model achieves up to 1.4x speedup in single-stream deployment and up to 3.0x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario. The following performance benchmarks were conducted with vLLM version 0.7.2, and GuideLLM.

guidellm --model neuralmagic/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server

Single-stream performance (measured with vLLM version 0.7.2)

**Use case profiles: prompt tokens / generation tokens

**QPD: Queries per dollar, based on on-demand cost at Lambda Labs (observed on 2/18/2025).

Multi-stream asynchronous performance (measured with vLLM version 0.7.2)

**Use case profiles: prompt tokens / generation tokens

**QPS: Queries per second.

**QPD: Queries per dollar, based on on-demand cost at Lambda Labs (observed on 2/18/2025).

📄 License

This project is licensed under the MIT License.