Text Generation
GGUF
mesh-llm
layer-package
skippy
distributed-inference
local-inference
openai-compatible
conversational
Instructions to use meshllm/DeepSeek-R1-Q4_K_M-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use meshllm/DeepSeek-R1-Q4_K_M-layers 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 meshllm/DeepSeek-R1-Q4_K_M-layers # Run inference directly in the terminal: llama cli -hf meshllm/DeepSeek-R1-Q4_K_M-layers
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf meshllm/DeepSeek-R1-Q4_K_M-layers # Run inference directly in the terminal: llama cli -hf meshllm/DeepSeek-R1-Q4_K_M-layers
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 meshllm/DeepSeek-R1-Q4_K_M-layers # Run inference directly in the terminal: ./llama-cli -hf meshllm/DeepSeek-R1-Q4_K_M-layers
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 meshllm/DeepSeek-R1-Q4_K_M-layers # Run inference directly in the terminal: ./build/bin/llama-cli -hf meshllm/DeepSeek-R1-Q4_K_M-layers
Use Docker
docker model run hf.co/meshllm/DeepSeek-R1-Q4_K_M-layers
- LM Studio
- Jan
- vLLM
How to use meshllm/DeepSeek-R1-Q4_K_M-layers with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meshllm/DeepSeek-R1-Q4_K_M-layers" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meshllm/DeepSeek-R1-Q4_K_M-layers", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/meshllm/DeepSeek-R1-Q4_K_M-layers
- Ollama
How to use meshllm/DeepSeek-R1-Q4_K_M-layers with Ollama:
ollama run hf.co/meshllm/DeepSeek-R1-Q4_K_M-layers
- Unsloth Studio
How to use meshllm/DeepSeek-R1-Q4_K_M-layers 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 meshllm/DeepSeek-R1-Q4_K_M-layers 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 meshllm/DeepSeek-R1-Q4_K_M-layers to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for meshllm/DeepSeek-R1-Q4_K_M-layers to start chatting
- Docker Model Runner
How to use meshllm/DeepSeek-R1-Q4_K_M-layers with Docker Model Runner:
docker model run hf.co/meshllm/DeepSeek-R1-Q4_K_M-layers
- Lemonade
How to use meshllm/DeepSeek-R1-Q4_K_M-layers with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull meshllm/DeepSeek-R1-Q4_K_M-layers
Run and chat with the model
lemonade run user.DeepSeek-R1-Q4_K_M-layers-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Add layer package manifest from unsloth/DeepSeek-R1-GGUF (unsloth/DeepSeek-R1-GGUF:DeepSeek-R1-Q4_K_M)
Browse files- model-package.json +578 -0
model-package.json
ADDED
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| 1 |
+
{
|
| 2 |
+
"schema_version": 1,
|
| 3 |
+
"model_id": "unsloth/DeepSeek-R1-GGUF:DeepSeek-R1-Q4_K_M",
|
| 4 |
+
"source_model": {
|
| 5 |
+
"path": "/source/DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00001-of-00009.gguf",
|
| 6 |
+
"sha256": "d111d9e28b4035e6781906b6451b7866737b4a4ee734baa1575c55d8aa1b4200",
|
| 7 |
+
"repo": "unsloth/DeepSeek-R1-GGUF",
|
| 8 |
+
"revision": "main",
|
| 9 |
+
"primary_file": "DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00001-of-00009.gguf",
|
| 10 |
+
"canonical_ref": "unsloth/DeepSeek-R1-GGUF@main/DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00001-of-00009.gguf",
|
| 11 |
+
"distribution_id": "DeepSeek-R1-Q4_K_M",
|
| 12 |
+
"files": [
|
| 13 |
+
{
|
| 14 |
+
"path": "DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00001-of-00009.gguf",
|
| 15 |
+
"size_bytes": null,
|
| 16 |
+
"sha256": null
|
| 17 |
+
},
|
| 18 |
+
{
|
| 19 |
+
"path": "DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00002-of-00009.gguf",
|
| 20 |
+
"size_bytes": null,
|
| 21 |
+
"sha256": null
|
| 22 |
+
},
|
| 23 |
+
{
|
| 24 |
+
"path": "DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00003-of-00009.gguf",
|
| 25 |
+
"size_bytes": null,
|
| 26 |
+
"sha256": null
|
| 27 |
+
},
|
| 28 |
+
{
|
| 29 |
+
"path": "DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00004-of-00009.gguf",
|
| 30 |
+
"size_bytes": null,
|
| 31 |
+
"sha256": null
|
| 32 |
+
},
|
| 33 |
+
{
|
| 34 |
+
"path": "DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00005-of-00009.gguf",
|
| 35 |
+
"size_bytes": null,
|
| 36 |
+
"sha256": null
|
| 37 |
+
},
|
| 38 |
+
{
|
| 39 |
+
"path": "DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00006-of-00009.gguf",
|
| 40 |
+
"size_bytes": null,
|
| 41 |
+
"sha256": null
|
| 42 |
+
},
|
| 43 |
+
{
|
| 44 |
+
"path": "DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00007-of-00009.gguf",
|
| 45 |
+
"size_bytes": null,
|
| 46 |
+
"sha256": null
|
| 47 |
+
},
|
| 48 |
+
{
|
| 49 |
+
"path": "DeepSeek-R1-Q4_K_M/DeepSeek-R1-Q4_K_M-00008-of-00009.gguf",
|
| 50 |
+
"size_bytes": null,
|
| 51 |
+
"sha256": null
|
| 52 |
+
},
|
| 53 |
+
{
|
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