Instructions to use Spadav/emma-5-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 Spadav/emma-5-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 Spadav/emma-5-GGUF:F16 # Run inference directly in the terminal: llama cli -hf Spadav/emma-5-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Spadav/emma-5-GGUF:F16 # Run inference directly in the terminal: llama cli -hf Spadav/emma-5-GGUF: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 Spadav/emma-5-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf Spadav/emma-5-GGUF: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 Spadav/emma-5-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Spadav/emma-5-GGUF:F16
Use Docker
docker model run hf.co/Spadav/emma-5-GGUF:F16
- LM Studio
- Jan
- vLLM
How to use Spadav/emma-5-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Spadav/emma-5-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Spadav/emma-5-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Spadav/emma-5-GGUF:F16
- Ollama
How to use Spadav/emma-5-GGUF with Ollama:
ollama run hf.co/Spadav/emma-5-GGUF:F16
- Unsloth Studio
How to use Spadav/emma-5-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Spadav/emma-5-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Spadav/emma-5-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Spadav/emma-5-GGUF to start chatting
- Docker Model Runner
How to use Spadav/emma-5-GGUF with Docker Model Runner:
docker model run hf.co/Spadav/emma-5-GGUF:F16
- Lemonade
How to use Spadav/emma-5-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Spadav/emma-5-GGUF:F16
Run and chat with the model
lemonade run user.emma-5-GGUF-F16
List all available models
lemonade list
- Atomic Chat
Emma-5 GGUF
Unofficial GGUF conversion of eldavoo/emma-5, archived from the original Egomnia Emma-5 release.
The conversion was validated by reconstructing the ONNX attention graph in PyTorch and comparing logits against the original ONNX Runtime output before exporting to GGUF.
The original ONNX model remains the reference runtime. This GGUF is intended for raw llama.cpp completion use, not chat-template frontends.
Files
| File | Description |
|---|---|
emma-5-f16.gguf |
FP16 GGUF, ~1.2 GB |
Conversion Details
The original model is distributed as ONNX only. Direct conversion to GGUF required matching the exported attention graph exactly.
Pipeline summary:
- ONNX weights and rotary constants were loaded.
- The exported attention graph was matched in PyTorch for validation.
- Logit parity was checked against ONNX Runtime.
- Q/K rows were permuted from split-half RoPE layout to llama.cpp interleaved layout.
- The model was exported as FP16 GGUF with
rms_norm_eps=1e-6.
Validation result:
max absolute logit error: ~4e-5
Architecture
| Parameter | Value |
|---|---|
| Parameters | ~550M |
| Layers | 28 |
| Hidden size | 1280 |
| Q heads | 16 |
| KV heads | 4 |
| Head dim | 80 |
| GQA ratio | 4:1 |
| FFN | SwiGLU |
| Context | 2048 |
| Vocab | 50000 |
| RoPE theta | 10000 |
| Embeddings | Tied |
Prompt Format
Use a single-line Italian instruction format:
### Istruzione: Qual e la capitale d Italia? ### Risposta:
Use raw completion mode only.
Do not use ChatML, Llama 3 chat templates, OpenAI-style messages, or frontend "conversation" wrappers. In particular, prompts containing markers like <|im_start|> or <|im_end|> are not supported and may cause repeated marker output.
Avoid multiline templates in llama.cpp. The original ONNX SentencePiece path normalizes whitespace differently, while llama.cpp may preserve newline tokens.
For the most faithful behavior, compare against or use the original ONNX Runtime model.
Example
./llama-cli \
-m emma-5-f16.gguf \
--no-conversation \
-p "### Istruzione: Qual e la capitale d Italia? ### Risposta:" \
-n 64 \
--temp 0.7 \
--repeat-penalty 1.2
Expected style:
La capitale d'Italia è Roma...
Quantization
This repo provides the FP16 GGUF. Other quantizations can be generated with llama.cpp:
./llama-quantize emma-5-f16.gguf emma-5-Q4_K_M.gguf Q4_K_M
Compatibility
Converted and smoke-tested with a recent llama.cpp build.
Credits
- Original model by Egomnia S.p.A.
- Archived by eldavoo
- GGUF conversion by Spadav
This is an unofficial conversion.
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Model tree for Spadav/emma-5-GGUF
Base model
eldavoo/emma-5