Instructions to use cortexso/internlm3-8b-it 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 cortexso/internlm3-8b-it 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 cortexso/internlm3-8b-it:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/internlm3-8b-it:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cortexso/internlm3-8b-it:Q4_K_M # Run inference directly in the terminal: llama cli -hf cortexso/internlm3-8b-it: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 cortexso/internlm3-8b-it:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/internlm3-8b-it: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 cortexso/internlm3-8b-it:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/internlm3-8b-it:Q4_K_M
Use Docker
docker model run hf.co/cortexso/internlm3-8b-it:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/internlm3-8b-it with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/internlm3-8b-it" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/internlm3-8b-it", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/internlm3-8b-it:Q4_K_M
- Ollama
How to use cortexso/internlm3-8b-it with Ollama:
ollama run hf.co/cortexso/internlm3-8b-it:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use cortexso/internlm3-8b-it with Docker Model Runner:
docker model run hf.co/cortexso/internlm3-8b-it:Q4_K_M
- Lemonade
How to use cortexso/internlm3-8b-it with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/internlm3-8b-it:Q4_K_M
Run and chat with the model
lemonade run user.internlm3-8b-it-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download internlm3-8b-instruct-q4_k_m.gguf from cortexso/internlm3-8b-it: direct link, hf CLI and curl.
- Browser
- Download file 5.36 GB
-
https://huggingface.co/cortexso/internlm3-8b-it/resolve/main/internlm3-8b-instruct-q4_k_m.gguf
- Command line
-
hf download hf://cortexso/internlm3-8b-it/internlm3-8b-instruct-q4_k_m.gguf
-
curl -L -o internlm3-8b-instruct-q4_k_m.gguf https://huggingface.co/cortexso/internlm3-8b-it/resolve/main/internlm3-8b-instruct-q4_k_m.gguf
5.36 GB
- Xet hash:
- 8106f62e9d35e45039be3d8e5e9f715847d8e15b8bd0661ad78f5fdf10c0adf2
- Size of remote file:
- 5.36 GB
- SHA256:
- a8e44f4d07587754795105b44faa720c8edc5c739caf254a144f5c9d1cf8d6b7
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