Instructions to use MexIvanov/zephyr-python-ru-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use MexIvanov/zephyr-python-ru-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MexIvanov/zephyr-python-ru-gguf", filename="zephyr-python-ru-q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use MexIvanov/zephyr-python-ru-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf MexIvanov/zephyr-python-ru-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MexIvanov/zephyr-python-ru-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 MexIvanov/zephyr-python-ru-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MexIvanov/zephyr-python-ru-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 MexIvanov/zephyr-python-ru-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MexIvanov/zephyr-python-ru-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 MexIvanov/zephyr-python-ru-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MexIvanov/zephyr-python-ru-gguf:Q4_K_M
Use Docker
docker model run hf.co/MexIvanov/zephyr-python-ru-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use MexIvanov/zephyr-python-ru-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MexIvanov/zephyr-python-ru-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MexIvanov/zephyr-python-ru-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MexIvanov/zephyr-python-ru-gguf:Q4_K_M
- Ollama
How to use MexIvanov/zephyr-python-ru-gguf with Ollama:
ollama run hf.co/MexIvanov/zephyr-python-ru-gguf:Q4_K_M
- Unsloth Studio new
How to use MexIvanov/zephyr-python-ru-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 MexIvanov/zephyr-python-ru-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 MexIvanov/zephyr-python-ru-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for MexIvanov/zephyr-python-ru-gguf to start chatting
- Docker Model Runner
How to use MexIvanov/zephyr-python-ru-gguf with Docker Model Runner:
docker model run hf.co/MexIvanov/zephyr-python-ru-gguf:Q4_K_M
- Lemonade
How to use MexIvanov/zephyr-python-ru-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MexIvanov/zephyr-python-ru-gguf:Q4_K_M
Run and chat with the model
lemonade run user.zephyr-python-ru-gguf-Q4_K_M
List all available models
lemonade list
Model Card for zephyr-python-ru-gguf
Model Details
Model Description
- Developed by: C.B. Pronin, A.V. Volosova, A.V. Ostroukh, Yu.N. Strogov, V.V. Kurbatov, A.S. Umarova.
- Model type: GGUF Conversion and quantizations of model "MexIvanov/zephyr-python-ru-merged" made for ease of inference.
- Language(s) (NLP): Russian, English, Python
- License: MIT
- Finetuned from model: HuggingFaceH4/zephyr-7b-beta
Model Sources
Uses
An experimental finetune of Zephyr-7b-beta, aimed at improving coding performance and support for coding-related instructions written in Russian language.
Direct Use
Instruction-based coding in Python, based of instructions written in natural language (English or Russian)
Prompt template - Zephyr:
<|system|>
</s>
<|user|>
{prompt}</s>
<|assistant|>
Provided files (quantization info taken from TheBloke/zephyr-7B-beta-GGUF)
| Name | Quant method | Bits | Use case |
|---|---|---|---|
| zephyr-python-ru-q4_K_M.gguf | Q4_K_M | 4 | medium, balanced quality - recommended |
| zephyr-python-ru-q6_K.gguf | Q6_K | 6 | very large, extremely low quality loss |
Bias, Risks, and Limitations
This adapter model is intended (but not limited) for research usage only. It was trained on a code based instruction set and it does not have any moderation mechanisms. Use at your own risk, we are not responsible for any usage or output of this model.
Quote from Zephyr (base-model) repository: "Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (mistralai/Mistral-7B-v0.1), however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this."
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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