Instructions to use nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic")
model = AutoModelForCausalLM.from_pretrained("nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic

SGLang

How to use nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic with Docker Model Runner:
```
docker model run hf.co/nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-dynamic
```

DeepSeek-R1-Distill-Llama-70B-FP8-dynamic / README.md

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	---
	license: apache-2.0
	language:
	- en
	tags:
	- deepseek
	- fp8
	- vllm
	base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
	library_name: transformers
	---

	# DeepSeek-R1-Distill-Llama-70B-FP8-Dynamic

	## Model Overview
	- Model Architecture: DeepSeek-R1-Distill-Llama-70B
	- Input: Text
	- Output: Text
	- Model Optimizations:
	- Weight quantization: FP8
	- Activation quantization: FP8
	- Release Date: 3/1/2025
	- Version: 1.0
	- Model Developers: Neural Magic

	Quantized version of [DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B).
	It achieves an average score of 76.52 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 76.49.

	### Model Optimizations

	This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM.
	This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized.

	## Deployment

	### Use with vLLM

	This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.

	```python
	from transformers import AutoTokenizer
	from vllm import LLM, SamplingParams

	max_model_len, tp_size = 4096, 1
	model_name = "nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-Dynamic"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
	sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])

	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](https://docs.vllm.ai/en/latest/) for more details.

	## Creation

	This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.


	```python
	import argparse
	from transformers import AutoModelForCausalLM, AutoTokenizer
	from llmcompressor.modifiers.quantization import QuantizationModifier
	from llmcompressor.transformers import oneshot
	import os

	def main():
	parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8')
	parser.add_argument('--model_id', type=str, required=True,
	help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")')
	parser.add_argument('--save_path', type=str, default='.',
	help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic')
	args = parser.parse_args()

	# Load model
	model = AutoModelForCausalLM.from_pretrained(
	args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True,
	)
	tokenizer = AutoTokenizer.from_pretrained(args.model_id)

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

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

	save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic")
	os.makedirs(save_path, exist_ok=True)

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

	if __name__ == "__main__":
	main()
	```

	## Evaluation

	The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) and [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/), using the following commands:

	OpenLLM Leaderboard V1:
	```
	lm_eval \
	--model vllm \
	--model_args pretrained="nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-Dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
	--tasks openllm \
	--write_out \
	--batch_size auto \
	--output_path output_dir \
	--show_config
	```

	OpenLLM Leaderboard V2:
	```
	lm_eval \
	--model vllm \
	--model_args pretrained="nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-Dynamic",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=2,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
	--apply_chat_template \
	--fewshot_as_multiturn \
	--tasks leaderboard \
	--write_out \
	--batch_size auto \
	--output_path output_dir \
	--show_config

	```

	### Accuracy

	#### OpenLLM Leaderboard V1 evaluation scores

	\| Metric \| deepseek-ai/DeepSeek-R1-Distill-Llama-70B \| nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-Dynamic \|
	\|-----------------------------------------\|:---------------------------------:\|:-------------------------------------------:\|
	\| ARC-Challenge (Acc-Norm, 25-shot) \| 66.38 \| 66.38 \|
	\| GSM8K (Strict-Match, 5-shot) \| 92.87 \| 93.25 \|
	\| HellaSwag (Acc-Norm, 10-shot) \| 85.41 \| 85.40 \|
	\| MMLU (Acc, 5-shot) \| 79.02 \| 78.84 \|
	\| TruthfulQA (MC2, 0-shot) \| 57.24 \| 57.54 \|
	\| Winogrande (Acc, 5-shot) \| 78.06 \| 77.74 \|
	\| Average Score \| 76.49 \| 76.52 \|
	\| Recovery (%) \| 100.00 \| 100.03 \|

	#### OpenLLM Leaderboard V2 evaluation scores


	\| Metric \| deepseek-ai/DeepSeek-R1-Distill-Llama-70B \| nm-testing/DeepSeek-R1-Distill-Llama-70B-FP8-Dynamic \|
	\|---------------------------------------------------------\|:---------------------------------:\|:-------------------------------------------:\|
	\| IFEval (Inst-and-Prompt Level Strict Acc, 0-shot) \| 43.51 \| 42.47 \|
	\| BBH (Acc-Norm, 3-shot) \| 35.30 \| 33.66 \|
	\| MMLU-Pro (Acc, 5-shot) \| 41.35 \| 41.05 \|
	\| Average Score \| 40.05 \| 39.06 \|
	\| Recovery (%) \| 100.00 \| 97.53 \|
	\| Math-Hard (Exact-Match, 4-shot) \| 5.55 \| 9.03 \|
	\| GPQA (Acc-Norm, 0-shot) \| 1.64 \| 1.58 \|
	\| MUSR (Acc-Norm, 0-shot) \| 13.28 \| 13.80 \|

	Results on Math-Hard, GPQA, and MUSR are not considred for accuracy recovery calculation because the unquantized model has close to random prediction accuracy which doesn't provide a reliable baseline for recovery calculation.