Instructions to use Felladrin/gguf-f16-Supra2-100M-Instruct 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 Felladrin/gguf-f16-Supra2-100M-Instruct 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 Felladrin/gguf-f16-Supra2-100M-Instruct:F16 # Run inference directly in the terminal: llama cli -hf Felladrin/gguf-f16-Supra2-100M-Instruct:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Felladrin/gguf-f16-Supra2-100M-Instruct:F16 # Run inference directly in the terminal: llama cli -hf Felladrin/gguf-f16-Supra2-100M-Instruct:F16
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 Felladrin/gguf-f16-Supra2-100M-Instruct:F16 # Run inference directly in the terminal: ./llama-cli -hf Felladrin/gguf-f16-Supra2-100M-Instruct:F16
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 Felladrin/gguf-f16-Supra2-100M-Instruct:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Felladrin/gguf-f16-Supra2-100M-Instruct:F16
Use Docker
docker model run hf.co/Felladrin/gguf-f16-Supra2-100M-Instruct:F16
- LM Studio
- Jan
- Ollama
How to use Felladrin/gguf-f16-Supra2-100M-Instruct with Ollama:
ollama run hf.co/Felladrin/gguf-f16-Supra2-100M-Instruct:F16
- Unsloth Desktop
- Docker Model Runner
How to use Felladrin/gguf-f16-Supra2-100M-Instruct with Docker Model Runner:
docker model run hf.co/Felladrin/gguf-f16-Supra2-100M-Instruct:F16
- Lemonade
How to use Felladrin/gguf-f16-Supra2-100M-Instruct with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Felladrin/gguf-f16-Supra2-100M-Instruct:F16
Run and chat with the model
lemonade run user.gguf-f16-Supra2-100M-Instruct-F16
List all available models
lemonade list
- Atomic Chat
gguf-f16-Supra2-100M-Instruct
GGUF conversion of SupraLabs/Supra2-100M-Instruct, produced with llama.cpp's convert_hf_to_gguf.py.
Supra2-100M-Instruct-F16.gguf: full-precision (F16) GGUF, unquantized, with the ChatML template embedded.
One thing here differs from a faithful conversion: the source declares </s> as its EOS token, while its chat template ends every turn with <|im_end|>. In this file <|im_end|> (id 6) is registered as both EOS and EOT, so a runtime stops at the end of the turn without needing extra stop strings. llama.cpp already stops there by itself (it treats the <|im_end|> name as end-of-generation, whatever the EOS metadata says), so this only matters for runtimes without that heuristic.
Everything else is untouched: all 134 tensors are byte-identical (sha256 per tensor) to the Supra2-100M-SFT-F16.gguf that SupraLabs publish in the source repo. If you run llama.cpp, either file gives you the same model.
Note: llama.cpp doesn't recognize this model's BPE pre-tokenizer, so the conversion sets it to qwen2, which does the same thing the source tokenizer does (Digits with individual digits, then ByteLevel). That's the same mapping the source GGUF uses.
See the source model card for training details, license terms and usage notes.
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Base model
SupraLabs/Supra2-100M-Base