Instructions to use HuggingFaceBio/Carbon-500M-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 HuggingFaceBio/Carbon-500M-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 HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf HuggingFaceBio/Carbon-500M-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 HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf HuggingFaceBio/Carbon-500M-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 HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf HuggingFaceBio/Carbon-500M-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 HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use HuggingFaceBio/Carbon-500M-GGUF with Ollama:
ollama run hf.co/HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use HuggingFaceBio/Carbon-500M-GGUF with Docker Model Runner:
docker model run hf.co/HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M
- Lemonade
How to use HuggingFaceBio/Carbon-500M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull HuggingFaceBio/Carbon-500M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Carbon-500M-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Cleanup: drop SHA256 column + remove stale Files section
Browse files
README.md
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## Available files
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| BF16 | `carbon-500m-bf16.gguf` | 982.2 MB |
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| Q8_0 | `carbon-500m-q8_0.gguf` | 524.6 MB |
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| Q6_K | `carbon-500m-q6_k.gguf` | 443.2 MB |
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| Q5_K_M | `carbon-500m-q5_k_m.gguf` | 405.1 MB |
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| Q4_K_M | `carbon-500m-q4_k_m.gguf` | 369.2 MB |
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K-quants are produced with `--token-embedding-type Q8_0 --output-tensor-type Q8_0`
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so the sensitive embedding/output tensors stay at Q8_0 precision (important for
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cd llama.cpp && cmake -B build && cmake --build build -j
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```
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## Files
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| `carbon-500m-bf16.gguf` | bf16 (lossless from source) | 983 MB |
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## Usage
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## Available files
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| BF16 | `carbon-500m-bf16.gguf` | 982.2 MB |
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| Q8_0 | `carbon-500m-q8_0.gguf` | 524.6 MB |
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| Q6_K | `carbon-500m-q6_k.gguf` | 443.2 MB |
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| Q5_K_M | `carbon-500m-q5_k_m.gguf` | 405.1 MB |
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| Q4_K_M | `carbon-500m-q4_k_m.gguf` | 369.2 MB |
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K-quants are produced with `--token-embedding-type Q8_0 --output-tensor-type Q8_0`
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so the sensitive embedding/output tensors stay at Q8_0 precision (important for
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cd llama.cpp && cmake -B build && cmake --build build -j
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```
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## Usage
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