Instructions to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF", filename="locutusquexfelladrin-tinymistral248m-instruct.fp16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-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 afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-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 afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
- Ollama
How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with Ollama:
ollama run hf.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-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 afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-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 afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF to start chatting
- Docker Model Runner
How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
- Lemonade
How to use afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF
Quantized GGUF model files for LocutusqueXFelladrin-TinyMistral248M-Instruct from Locutusque
| Name | Quant method | Size |
|---|---|---|
| locutusquexfelladrin-tinymistral248m-instruct.fp16.gguf | fp16 | 497.76 MB |
| locutusquexfelladrin-tinymistral248m-instruct.q2_k.gguf | q2_k | 116.20 MB |
| locutusquexfelladrin-tinymistral248m-instruct.q3_k_m.gguf | q3_k_m | 131.01 MB |
| locutusquexfelladrin-tinymistral248m-instruct.q4_k_m.gguf | q4_k_m | 156.61 MB |
| locutusquexfelladrin-tinymistral248m-instruct.q5_k_m.gguf | q5_k_m | 180.17 MB |
| locutusquexfelladrin-tinymistral248m-instruct.q6_k.gguf | q6_k | 205.20 MB |
| locutusquexfelladrin-tinymistral248m-instruct.q8_0.gguf | q8_0 | 265.26 MB |
Original Model Card:
LocutusqueXFelladrin-TinyMistral248M-Instruct
This model was created by merging Locutusque/TinyMistral-248M-Instruct and Felladrin/TinyMistral-248M-SFT-v4 using mergekit. After the two models were merged, the resulting model was further trained on ~20,000 examples on the Locutusque/inst_mix_v2_top_100k at a low learning rate to further normalize weights. The following is the YAML config used to merge:
models:
- model: Felladrin/TinyMistral-248M-SFT-v4
parameters:
weight: 0.5
- model: Locutusque/TinyMistral-248M-Instruct
parameters:
weight: 1.0
merge_method: linear
dtype: float16
The resulting model combines the best of both worlds. With Locutusque/TinyMistral-248M-Instruct's coding capabilities and reasoning skills, and Felladrin/TinyMistral-248M-SFT-v4's low hallucination and instruction-following capabilities. The resulting model has an incredible performance considering its size.
Evaluation
Coming soon...
- Downloads last month
- 55