Instructions to use mradermacher/internlm2-math-base-7b-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/internlm2-math-base-7b-i1-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/internlm2-math-base-7b-i1-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/internlm2-math-base-7b-i1-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 mradermacher/internlm2-math-base-7b-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/internlm2-math-base-7b-i1-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 mradermacher/internlm2-math-base-7b-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/internlm2-math-base-7b-i1-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 mradermacher/internlm2-math-base-7b-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/internlm2-math-base-7b-i1-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 mradermacher/internlm2-math-base-7b-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/internlm2-math-base-7b-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/internlm2-math-base-7b-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/internlm2-math-base-7b-i1-GGUF with Ollama:
ollama run hf.co/mradermacher/internlm2-math-base-7b-i1-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/internlm2-math-base-7b-i1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/internlm2-math-base-7b-i1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/internlm2-math-base-7b-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/internlm2-math-base-7b-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.internlm2-math-base-7b-i1-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download internlm2-math-base-7b.i1-IQ2_S.gguf from mradermacher/internlm2-math-base-7b-i1-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 2.59 GB
-
https://huggingface.co/mradermacher/internlm2-math-base-7b-i1-GGUF/resolve/main/internlm2-math-base-7b.i1-IQ2_S.gguf
- Command line
-
hf download hf://mradermacher/internlm2-math-base-7b-i1-GGUF/internlm2-math-base-7b.i1-IQ2_S.gguf
-
curl -L -o internlm2-math-base-7b.i1-IQ2_S.gguf https://huggingface.co/mradermacher/internlm2-math-base-7b-i1-GGUF/resolve/main/internlm2-math-base-7b.i1-IQ2_S.gguf
2.59 GB
- Xet hash:
- a351d065cdecbf6fa7a5699f720f8536ded998947567b6c10d17c11a35276534
- Size of remote file:
- 2.59 GB
- SHA256:
- 247bfbf71be384dd15df11672e2d75b1930d5eaca76959fac294c6dcb109827d
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