Text Generation
Transformers
GGUF
English
llama
deepseek
unsloth
llama-3
meta
imatrix
conversational
Instructions to use unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF") model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unsloth/DeepSeek-R1-Distill-Llama-70B-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 unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF: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-Distill-Llama-70B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF: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-Distill-Llama-70B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF:UD-Q4_K_XL
- Llama new
- LM Studio
- Jan
- vLLM
How to use unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/DeepSeek-R1-Distill-Llama-70B-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": "unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF:UD-Q4_K_XL
- SGLang
How to use unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF 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-Distill-Llama-70B-GGUF" \ --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-Distill-Llama-70B-GGUF", "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-Distill-Llama-70B-GGUF" \ --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-Distill-Llama-70B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF with Ollama:
ollama run hf.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF:UD-Q4_K_XL
- Unsloth Desktop
- Docker Model Runner
How to use unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.DeepSeek-R1-Distill-Llama-70B-GGUF-UD-Q4_K_XL
List all available models
lemonade list
- Atomic Chat
Download DeepSeek-R1-Distill-Llama-70B-UD-IQ3_XXS.gguf from unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 27.7 GB
-
https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-70B-UD-IQ3_XXS.gguf
- Command line
-
hf download hf://unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF/DeepSeek-R1-Distill-Llama-70B-UD-IQ3_XXS.gguf
-
curl -L -o DeepSeek-R1-Distill-Llama-70B-UD-IQ3_XXS.gguf https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-70B-UD-IQ3_XXS.gguf
27.7 GB
- Xet hash:
- 800a1a76c6cb748bda312755fd4ad47bf1336e400fc9c8af55087e8715bf6bf8
- Size of remote file:
- 27.7 GB
- SHA256:
- e37a68b01a41725a5b533bec25fecae4e7c3ff2535389289ffa0d2ad460af5b5
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