Transformers
PyTorch
Safetensors
GGUF
qwen2
unsloth
trl
sft
text-generation-inference
conversational
Instructions to use Lemonsion/DeepSeek-R1-traffic-COT-lll with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lemonsion/DeepSeek-R1-traffic-COT-lll with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lemonsion/DeepSeek-R1-traffic-COT-lll", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Lemonsion/DeepSeek-R1-traffic-COT-lll 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 Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0 # Run inference directly in the terminal: llama cli -hf Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0 # Run inference directly in the terminal: llama cli -hf Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0
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 Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0
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 Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0
Use Docker
docker model run hf.co/Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0
- Llama new
- LM Studio
- Jan
- Ollama
How to use Lemonsion/DeepSeek-R1-traffic-COT-lll with Ollama:
ollama run hf.co/Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use Lemonsion/DeepSeek-R1-traffic-COT-lll with Docker Model Runner:
docker model run hf.co/Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0
- Lemonade
How to use Lemonsion/DeepSeek-R1-traffic-COT-lll with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lemonsion/DeepSeek-R1-traffic-COT-lll:Q8_0
Run and chat with the model
lemonade run user.DeepSeek-R1-traffic-COT-lll-Q8_0
List all available models
lemonade list
- Atomic Chat
Download tokenizer.json from Lemonsion/DeepSeek-R1-traffic-COT-lll: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Lemonsion/DeepSeek-R1-traffic-COT-lll/resolve/main/tokenizer.json
- Command line
-
hf download hf://Lemonsion/DeepSeek-R1-traffic-COT-lll/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Lemonsion/DeepSeek-R1-traffic-COT-lll/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 78af695675c4fc8453e45a039d33450bec835340edcdac08f9a1af71f046ab0d
- Size of remote file:
- 11.4 MB
- SHA256:
- e20ddafc659ba90242154b55275402edeca0715e5dbb30f56815a4ce081f4893
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.