Instructions to use neopolita/yi-coder-9b-gguf 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 neopolita/yi-coder-9b-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 neopolita/yi-coder-9b-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf neopolita/yi-coder-9b-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 neopolita/yi-coder-9b-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf neopolita/yi-coder-9b-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 neopolita/yi-coder-9b-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf neopolita/yi-coder-9b-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 neopolita/yi-coder-9b-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf neopolita/yi-coder-9b-gguf:Q4_K_M
Use Docker
docker model run hf.co/neopolita/yi-coder-9b-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use neopolita/yi-coder-9b-gguf with Ollama:
ollama run hf.co/neopolita/yi-coder-9b-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use neopolita/yi-coder-9b-gguf with Docker Model Runner:
docker model run hf.co/neopolita/yi-coder-9b-gguf:Q4_K_M
- Lemonade
How to use neopolita/yi-coder-9b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull neopolita/yi-coder-9b-gguf:Q4_K_M
Run and chat with the model
lemonade run user.yi-coder-9b-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
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---
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{}
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---
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# GGUF quants for [**01-ai/Yi-Coder-9B**](https://huggingface.co/01-ai/Yi-Coder-9B) using [llama.cpp](https://github.com/ggerganov/llama.cpp)
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**Terms of Use**: Please check the [**original model**](https://huggingface.co/01-ai/Yi-Coder-9B)
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<picture>
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<img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png">
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</picture>
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## Quants
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* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
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* `q3_k_s`: Uses Q3_K for all tensors
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* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
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* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
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* `q4_0`: Original quant method, 4-bit.
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* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
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* `q4_k_s`: Uses Q4_K for all tensors
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* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
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* `q5_0`: Higher accuracy, higher resource usage and slower inference.
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* `q5_1`: Even higher accuracy, resource usage and slower inference.
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* `q5_k_s`: Uses Q5_K for all tensors
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* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
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* `q6_k`: Uses Q8_K for all tensors
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* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.
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