Instructions to use unsloth/DeepSeek-R1-GGUF-UD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/DeepSeek-R1-GGUF-UD with Transformers:

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

pipe = pipeline("text-generation", model="unsloth/DeepSeek-R1-GGUF-UD", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unsloth/DeepSeek-R1-GGUF-UD", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-GGUF-UD", trust_remote_code=True)

llama-cpp-python

How to use unsloth/DeepSeek-R1-GGUF-UD with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/DeepSeek-R1-GGUF-UD",
	filename="BF16/DeepSeek-R1-BF16-00001-of-00030.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use unsloth/DeepSeek-R1-GGUF-UD with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use unsloth/DeepSeek-R1-GGUF-UD with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL

SGLang

How to use unsloth/DeepSeek-R1-GGUF-UD 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 "unsloth/DeepSeek-R1-GGUF-UD" \
    --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": "unsloth/DeepSeek-R1-GGUF-UD",
		"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 "unsloth/DeepSeek-R1-GGUF-UD" \
        --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": "unsloth/DeepSeek-R1-GGUF-UD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use unsloth/DeepSeek-R1-GGUF-UD with Ollama:
```
ollama run hf.co/unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL
```

Unsloth Studio new

How to use unsloth/DeepSeek-R1-GGUF-UD with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/DeepSeek-R1-GGUF-UD to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for unsloth/DeepSeek-R1-GGUF-UD to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/DeepSeek-R1-GGUF-UD to start chatting

Docker Model Runner
How to use unsloth/DeepSeek-R1-GGUF-UD with Docker Model Runner:
```
docker model run hf.co/unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL
```

Lemonade

How to use unsloth/DeepSeek-R1-GGUF-UD with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/DeepSeek-R1-GGUF-UD:UD-Q4_K_XL

Run and chat with the model

lemonade run user.DeepSeek-R1-GGUF-UD-UD-Q4_K_XL

List all available models

lemonade list

How can we use vllm to serve this gguf?

by pty819 - opened Apr 25, 2025

Discussion

pty819

Apr 25, 2025

Hi I tried to do as a topic in the vllm issues, someone said this was fixed. I just download the 2.51bit gguf and then download the tokenizers and change the torch_dtype to float16, but this still reports deepseek v2 gguf was not supported by vllm. Can you help me how to run this by vllm 0.8.4?

root@ubuntu:/data/vllm-serve# uv run vllm serve /data/upload_files/deepseek-R1.gguf --tokenizer /data/upload_files/R1_tokenizer/
INFO 04-25 19:27:48 [init.py:239] Automatically detected platform cuda.
INFO 04-25 19:27:49 [api_server.py:1034] vLLM API server version 0.8.4
INFO 04-25 19:27:49 [api_server.py:1035] args: Namespace(subparser='serve', model_tag='/data/upload_files/deepseek-R1.gguf', config='', host=None, port=8000, uvicorn_log_level='info', disable_uvicorn_access_log=False, allow_credentials=False, allowed_origins=[''], allowed_methods=[''], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template=None, chat_template_content_format='auto', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, enable_ssl_refresh=False, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_request_id_headers=False, enable_auto_tool_choice=False, tool_call_parser=None, tool_parser_plugin='', model='/data/upload_files/deepseek-R1.gguf', task='auto', tokenizer='/data/upload_files/R1_tokenizer/', hf_config_path=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, allowed_local_media_path=None, load_format='auto', download_dir=None, model_loader_extra_config=None, use_tqdm_on_load=True, config_format=<ConfigFormat.AUTO: 'auto'>, dtype='auto', kv_cache_dtype='auto', max_model_len=None, guided_decoding_backend='auto', logits_processor_pattern=None, model_impl='auto', distributed_executor_backend=None, pipeline_parallel_size=1, tensor_parallel_size=1, data_parallel_size=1, enable_expert_parallel=False, max_parallel_loading_workers=None, ray_workers_use_nsight=False, disable_custom_all_reduce=False, block_size=None, enable_prefix_caching=None, prefix_caching_hash_algo='builtin', disable_sliding_window=False, use_v2_block_manager=True, num_lookahead_slots=0, seed=None, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_partial_prefills=1, max_long_partial_prefills=1, long_prefill_token_threshold=0, max_num_seqs=None, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, hf_token=None, hf_overrides=None, enforce_eager=False, max_seq_len_to_capture=8192, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, disable_mm_preprocessor_cache=False, enable_lora=False, enable_lora_bias=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, scheduling_policy='fcfs', scheduler_cls='vllm.core.scheduler.Scheduler', override_neuron_config=None, override_pooler_config=None, compilation_config=None, kv_transfer_config=None, worker_cls='auto', worker_extension_cls='', generation_config='auto', override_generation_config=None, enable_sleep_mode=False, calculate_kv_scales=False, additional_config=None, enable_reasoning=False, reasoning_parser=None, disable_cascade_attn=False, disable_chunked_mm_input=False, disable_log_requests=False, max_log_len=None, disable_fastapi_docs=False, enable_prompt_tokens_details=False, enable_server_load_tracking=False, dispatch_function=<function ServeSubcommand.cmd at 0x7f8f4f6016c0>)
Traceback (most recent call last):
File "/mnt/data/vllm-serve/.venv/bin/vllm", line 10, in
sys.exit(main())
^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/vllm/entrypoints/cli/main.py", line 51, in main
args.dispatch_function(args)
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/vllm/entrypoints/cli/serve.py", line 27, in cmd
uvloop.run(run_server(args))
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/uvloop/init.py", line 109, in run
return __asyncio.run(
^^^^^^^^^^^^^^
File "/data/python/python3.12.9/install/lib/python3.12/asyncio/runners.py", line 195, in run
return runner.run(main)
^^^^^^^^^^^^^^^^
File "/data/python/python3.12.9/install/lib/python3.12/asyncio/runners.py", line 118, in run
return self._loop.run_until_complete(task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/uvloop/init.py", line 61, in wrapper
return await main
^^^^^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/vllm/entrypoints/openai/api_server.py", line 1069, in run_server
async with build_async_engine_client(args) as engine_client:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/python/python3.12.9/install/lib/python3.12/contextlib.py", line 210, in aenter
return await anext(self.gen)
^^^^^^^^^^^^^^^^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/vllm/entrypoints/openai/api_server.py", line 146, in build_async_engine_client
async with build_async_engine_client_from_engine_args(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/python/python3.12.9/install/lib/python3.12/contextlib.py", line 210, in aenter
return await anext(self.gen)
^^^^^^^^^^^^^^^^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/vllm/entrypoints/openai/api_server.py", line 166, in build_async_engine_client_from_engine_args
vllm_config = engine_args.create_engine_config(usage_context=usage_context)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/vllm/engine/arg_utils.py", line 1154, in create_engine_config
model_config = self.create_model_config()
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/vllm/engine/arg_utils.py", line 1042, in create_model_config
return ModelConfig(
^^^^^^^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/vllm/config.py", line 423, in init
hf_config = get_config(self.hf_config_path or self.model,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/vllm/transformers_utils/config.py", line 286, in get_config
config_dict, _ = PretrainedConfig.get_config_dict(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/transformers/configuration_utils.py", line 590, in get_config_dict
config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/transformers/configuration_utils.py", line 681, in _get_config_dict
config_dict = load_gguf_checkpoint(resolved_config_file, return_tensors=False)["config"]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/data/vllm-serve/.venv/lib/python3.12/site-packages/transformers/modeling_gguf_pytorch_utils.py", line 401, in load_gguf_checkpoint
raise ValueError(f"GGUF model with architecture {architecture} is not supported yet.")
ValueError: GGUF model with architecture deepseek2 is not supported yet.

pty819

Apr 25, 2025

the issue:
https://github.com/vllm-project/vllm/pull/13167

pty819

Apr 25, 2025

finally i know: uv run vllm serve /data/upload_files/deepseek-R1.gguf --enable-reasoning --reasoning-parser deepseek_r1 --hf-config-path /data/upload_files/R1_tokenizer/ --tokenizer /data/upload_files/R1_tokenizer/ --tensor-parallel-size 8 --host 0.0.0.0 --port 55556 --gpu-memory-utilization 0.91

shimmyshimmer

Unsloth AI org Apr 26, 2025

finally i know: uv run vllm serve /data/upload_files/deepseek-R1.gguf --enable-reasoning --reasoning-parser deepseek_r1 --hf-config-path /data/upload_files/R1_tokenizer/ --tokenizer /data/upload_files/R1_tokenizer/ --tensor-parallel-size 8 --host 0.0.0.0 --port 55556 --gpu-memory-utilization 0.91

Wait so it works? Oh wow I had no idea. I think I'll let everyone know to anyone else having the problem thank you

pty819

Apr 27, 2025

maybe you can let someone to try to load that gguf to vllm with a h800 x 8 machine... in my rtx a6000 *8 machine it reports no video memory fault but not any other faults.
Can you provide the safetensor files so we can use vllm to load that directly?

pty819

Apr 27, 2025

finally i know: uv run vllm serve /data/upload_files/deepseek-R1.gguf --enable-reasoning --reasoning-parser deepseek_r1 --hf-config-path /data/upload_files/R1_tokenizer/ --tokenizer /data/upload_files/R1_tokenizer/ --tensor-parallel-size 8 --host 0.0.0.0 --port 55556 --gpu-memory-utilization 0.91

Wait so it works? Oh wow I had no idea. I think I'll let everyone know to anyone else having the problem thank you

shimmyshimmer

Unsloth AI org Apr 29, 2025

finally i know: uv run vllm serve /data/upload_files/deepseek-R1.gguf --enable-reasoning --reasoning-parser deepseek_r1 --hf-config-path /data/upload_files/R1_tokenizer/ --tokenizer /data/upload_files/R1_tokenizer/ --tensor-parallel-size 8 --host 0.0.0.0 --port 55556 --gpu-memory-utilization 0.91

Wait so it works? Oh wow I had no idea. I think I'll let everyone know to anyone else having the problem thank you

maybe you can let someone to try to load that gguf to vllm with a h800 x 8 machine... in my rtx a6000 *8 machine it reports no video memory fault but not any other faults.
Can you provide the safetensor files so we can use vllm to load that directly?

isnt this the safetensor or do you mean the dynamic versions? Im not sure if it can even work for safetensor conversion apologies

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