Instructions to use bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF",
	filename="DeepSeek-R1-Distill-Qwen-32B-IQ2_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M

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 bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M

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 bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M

Ollama
How to use bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF with Ollama:
```
ollama run hf.co/bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M
```

Unsloth Studio

How to use bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF 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 bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF 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 bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Qwen-32B-GGUF-Q4_K_M

List all available models

lemonade list

deployment error with lmdeploy: RuntimeError: Could not find model architecture from config

#10

by ismailyenigul - opened Jan 28, 2025

Discussion

ismailyenigul

Jan 28, 2025

Hi,
When I try to run this model with lmdeploy as a pod on AWS EKS , getting the following error:

# lmdeploy serve api_server    bartowski/DeepSeek-R1-Distill-Qwen-32B-GGUF

File "/opt/lmdeploy/lmdeploy/cli/entrypoint.py", line 42, in run
    args.run(args)
  File "/opt/lmdeploy/lmdeploy/cli/serve.py", line 303, in api_server
    backend = autoget_backend(args.model_path)
  File "/opt/lmdeploy/lmdeploy/archs.py", line 42, in autoget_backend
    turbomind_has = is_supported_turbomind(model_path)
  File "/opt/lmdeploy/lmdeploy/turbomind/supported_models.py", line 86, in is_supported
    arch, cfg = get_model_arch(model_path)
  File "/opt/lmdeploy/lmdeploy/archs.py", line 194, in get_model_arch
    raise RuntimeError(
RuntimeError: Could not find model architecture from config: {'vocab_size': 50265, 'max_position_embeddings': 1024, 'd_model': 1024, 'encoder_ffn_dim'
: 4096, 'encoder_layers': 12, 'encoder_attention_heads': 16, 'decoder_ffn_dim': 4096, 'decoder_layers': 12, 'decoder_attention_heads': 16, 'dropout': 0.1, 

'attention_dropout': 0.0, 'activation_dropout': 0.0, 'activation_function': 'gelu', 'init_std': 0.02, 'encoder_layerdrop': 0.0, 'decoder_layerdrop': 0.0, 

'classifier_dropout': 0.0, 'use_cache': True, 'num_hidden_layers': 12, 'scale_embedding': False, 'return_dict': True, 'output_hidden_states': False, 

'output_attentions': False, 'torchscript': False, 'torch_dtype': None, 'use_bfloat16': False, 'tf_legacy_loss': False, 'pruned_heads': {}, 'tie_word_embeddings': 

True, 'chunk_size_feed_forward': 0, 'is_encoder_decoder': True, 'is_decoder': False, 'cross_attention_hidden_size': None, 'add_cross_attention': False, 

'tie_encoder_decoder': False, 'max_length': 20, 'min_length': 0, 'do_sample': False, 'early_stopping': False, 'num_beams': 1, 'num_beam_groups': 1, 

'diversity_penalty': 0.0, 'temperature': 1.0, 'top_k': 50, 'top_p': 1.0, 'typical_p': 1.0, 'repetition_penalty': 1.0, 'length_penalty': 1.0, 'no_repeat_ngram_size': 0, 

'encoder_no_repeat_ngram_size': 0, 'bad_words_ids': None, 'num_return_sequences': 1, 'output_scores': False, 'return_dict_in_generate': False, 

'forced_bos_token_id': None, 'forced_eos_token_id': 2, 'remove_invalid_values': False, 'exponential_decay_length_penalty': None, 'suppress_tokens': None, 

'begin_suppress_tokens': None, 'architectures': None, 'finetuning_task': None, 'id2label': {0: 'LABEL_0', 1: 'LABEL_1', 2: 'LABEL_2'}, 'label2id': {'LABEL_0': 0, 

'LABEL_1': 1, 'LABEL_2': 2}, 'tokenizer_class': None, 'prefix': None, 'bos_token_id': 0, 'pad_token_id': 1, 'eos_token_id': 2, 'sep_token_id': None, 

'decoder_start_token_id': 2, 'task_specific_params': None, 'problem_type': None, '_name_or_path': '/root/.cache/huggingface/hub/models--bartowski--

DeepSeek-R1-Distill-Qwen-32B-GGUF/snapshots/1dc8cf9ffa5dd333057ea1b09ccf4772d8726dec', '_attn_implementation_autoset': False, 'transformers_version': '4.48.0', 'model_type': 'bart'}

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