Instructions to use alrobles/EcoCoder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use alrobles/EcoCoder-7B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="alrobles/EcoCoder-7B", filename="ecocoder-7b-q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use alrobles/EcoCoder-7B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf alrobles/EcoCoder-7B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf alrobles/EcoCoder-7B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf alrobles/EcoCoder-7B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf alrobles/EcoCoder-7B: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 alrobles/EcoCoder-7B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf alrobles/EcoCoder-7B: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 alrobles/EcoCoder-7B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf alrobles/EcoCoder-7B:Q4_K_M
Use Docker
docker model run hf.co/alrobles/EcoCoder-7B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use alrobles/EcoCoder-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "alrobles/EcoCoder-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alrobles/EcoCoder-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/alrobles/EcoCoder-7B:Q4_K_M
- Ollama
How to use alrobles/EcoCoder-7B with Ollama:
ollama run hf.co/alrobles/EcoCoder-7B:Q4_K_M
- Unsloth Studio
How to use alrobles/EcoCoder-7B 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 alrobles/EcoCoder-7B 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 alrobles/EcoCoder-7B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for alrobles/EcoCoder-7B to start chatting
- Pi
How to use alrobles/EcoCoder-7B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf alrobles/EcoCoder-7B:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "alrobles/EcoCoder-7B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use alrobles/EcoCoder-7B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf alrobles/EcoCoder-7B:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default alrobles/EcoCoder-7B:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use alrobles/EcoCoder-7B with Docker Model Runner:
docker model run hf.co/alrobles/EcoCoder-7B:Q4_K_M
- Lemonade
How to use alrobles/EcoCoder-7B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull alrobles/EcoCoder-7B:Q4_K_M
Run and chat with the model
lemonade run user.EcoCoder-7B-Q4_K_M
List all available models
lemonade list
File size: 737 Bytes
a82f2e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | ---
license: mit
language:
- en
tags:
- qwen
- qwen-coder
- lora
- code
- ecology
- sdm
- gguf
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
pipeline_tag: text-generation
---
# EcoCoder-7B
LoRA fine-tune of Qwen2.5-Coder-7B-Instruct for ecological
Species Distribution Modeling (SDM) code generation.
- **Base model**: Qwen/Qwen2.5-Coder-7B-Instruct
- **LoRA rank**: 16
- **LoRA alpha**: 32
- **Training loss (final)**: ~0.13
- **Accuracy (eval)**: ~96%
- **Format**: GGUF Q4_K_M (4.4 GB)
- **Quantization**: llama.cpp Q4_K_M
## Usage with LM Studio
Search `alrobles/EcoCoder-7B` in LM Studio or download the GGUF.
## Usage with llama.cpp
```bash
llama-cli -m ecocoder-7b-q4_k_m.gguf -p "Write Python code to..."
```
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