Text Generation
MLX
Safetensors
Rust
qwen2
7b
agentic-coding
android
apple-silicon
attested
bash
c
chain-of-custody
chinese
code
code-completion
code-generation
code-infill
compacted
compensation-lora
consumer-gpu
cpp
cryptographically-verified
css
distillation
edge-inference
efficient
embedded
english
forge-alloy
function-calling
general
general-purpose
go
head-pruning
html
iphone
java
javascript
knowledge-distillation
kotlin
llama-cpp
lm-studio
local-inference
lora
macbook
mobile
multilingual
ollama
on-device
optimized
php
pruned
python
qwen
qwen-coder
qwen2.5
qwen2.5-coder
raspberry-pi
reproducible
ruby
sql
swift
teacher-student
typescript
validation-artifact
versatile
conversational
Instructions to use continuum-ai/qwen2.5-coder-7b-compacted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use continuum-ai/qwen2.5-coder-7b-compacted with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("continuum-ai/qwen2.5-coder-7b-compacted") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use continuum-ai/qwen2.5-coder-7b-compacted with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "continuum-ai/qwen2.5-coder-7b-compacted" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use continuum-ai/qwen2.5-coder-7b-compacted with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted"
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 continuum-ai/qwen2.5-coder-7b-compacted
Run Hermes
hermes
- MLX LM
How to use continuum-ai/qwen2.5-coder-7b-compacted with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "continuum-ai/qwen2.5-coder-7b-compacted"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "continuum-ai/qwen2.5-coder-7b-compacted" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "continuum-ai/qwen2.5-coder-7b-compacted", "messages": [ {"role": "user", "content": "Hello"} ] }'
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