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

Libraries

How to use deepseek-ai/DeepSeek-R1-Distill-Llama-70B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Llama-70B")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Llama-70B")
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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepseek-ai/DeepSeek-R1-Distill-Llama-70B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B

SGLang

How to use deepseek-ai/DeepSeek-R1-Distill-Llama-70B 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 "deepseek-ai/DeepSeek-R1-Distill-Llama-70B" \
    --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": "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
		"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 "deepseek-ai/DeepSeek-R1-Distill-Llama-70B" \
        --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": "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepseek-ai/DeepSeek-R1-Distill-Llama-70B with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B
```

Amazon Sagemaker deployment failing with CUDA OutOfMemory error

#10

by neelkapadia - opened Jan 28, 2025

Discussion

neelkapadia

Jan 28, 2025

•

edited Jan 28, 2025

Error Message:

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 896.00 MiB. GPU 
 #033[2m#033[3mrank#033[0m#033[2m=#033[0m0#033[0m

Code (referred from example code when you click Deploy):

hub = {
    'HF_MODEL_ID':'deepseek-ai/DeepSeek-R1-Distill-Llama-70B',
    'SM_NUM_GPUS': json.dumps(1)
}



# create Hugging Face Model Class
huggingface_model = HuggingFaceModel(
    image_uri=get_huggingface_llm_image_uri("huggingface",version="2.2.0"),
    env=hub,
    role=role, 
)

# deploy model to SageMaker Inference
predictor = huggingface_model.deploy(
    initial_instance_count=1,
    instance_type="ml.g5.2xlarge",
    container_startup_health_check_timeout=300,
  )

Config file from logs:

text_generation_launcher#033[0m#033[2m:#033[0m Args {
    model_id: "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
    revision: None,
    validation_workers: 2,
    sharded: None,
    num_shard: Some(
        1,
    ),
    quantize: None,
    speculate: None,
    dtype: None,
    trust_remote_code: false,
    max_concurrent_requests: 128,
    max_best_of: 2,
    max_stop_sequences: 4,
    max_top_n_tokens: 5,
    max_input_tokens: None,
    max_input_length: None,
    max_total_tokens: None,
    waiting_served_ratio: 0.3,
    max_batch_prefill_tokens: None,
    max_batch_total_tokens: None,
    max_waiting_tokens: 20,
    max_batch_size: None,
    cuda_graphs: None,
    hostname: "container-0.local",
    port: 8080,
    shard_uds_path: "/tmp/text-generation-server",
    master_addr: "localhost",
    master_port: 29500,
    huggingface_hub_cache: Some(
        "/tmp",
    ),
    weights_cache_override: None,
    disable_custom_kernels: false,
    cuda_memory_fraction: 1.0,
    rope_scaling: None,
    rope_factor: None,
    json_output: false,
    otlp_endpoint: None,
    otlp_service_name: "text-generation-inference.router",
    cors_allow_origin: [],
    watermark_gamma: None,
    watermark_delta: None,
    ngrok: false,
    ngrok_authtoken: None,
    ngrok_edge: None,
    tokenizer_config_path: None,
    disable_grammar_support: false,
    env: false,
    max_client_batch_size: 4,
    lora_adapters: None,
    disable_usage_stats: false,
    disable_crash_reports: false,
}

Tried on ml.g5.2xlarge, ml.g5.8xlarge, and ml.p4d.24xlarge. Getting the same error on all of them.

pagezyhf

Jan 28, 2025

Try with this. We shipped a new DLC with TGI v3 that is not yet referenced in Sagemaker SDK. Like this it should work. Change the region in the Image URI!
It should fit on g6.12xlarge and above.

import time
import sagemaker
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
import json

sagemaker_session = sagemaker.Session()
region = sagemaker_session.boto_region_name
role = sagemaker.get_execution_role()

image_uri = "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference:2.4.0-tgi3.0.1-gpu-py311-cu124-ubuntu22.04"
model_name = "deepseek-ai-deepseek-r1-distill-llama-70b" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())

hub = {
'HF_MODEL_ID': 'deepseek-ai/DeepSeek-R1-Distill-Llama-70B',
'SM_NUM_GPUS': json.dumps(8),
'MESSAGES_API_ENABLED': "true",
}

model = HuggingFaceModel(
name=model_name,
env=hub,
role=role,
image_uri=image_uri
)

predictor = model.deploy(
initial_instance_count=1,
instance_type="ml.g6.48xlarge",
endpoint_name=model_name
)

pagezyhf

Jan 28, 2025

Thanks for flagging. We'll update the deploy snippet :)

dabockster

Feb 7, 2025

Or just build an AI rig. These SaaS platforms may not continue to play nice with larger models in the future.

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