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Outlier-Ai
/
Outlier-Code-27B-MLX-4bit

Text Generation
MLX
Safetensors
qwen3_5
4-bit precision
4bit
agentic
apple-silicon
chat
chatgpt-alternative
claude-alternative
code
code-completion
code-generation
coding
conversational
edge-ai
function-calling
humaneval
instruct
local-llm
m1
m2
m3
m4
mac
mac-mini
mac-studio
macbook-air
macbook-pro
macos
metal
mlx-lm
no-cloud
offline
on-device
outlier
outlier-app
private
private-ai
quantized
qwen
qwen3.6
reasoning
thinking
tool-use
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use Outlier-Ai/Outlier-Code-27B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • MLX

    How to use Outlier-Ai/Outlier-Code-27B-MLX-4bit 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("Outlier-Ai/Outlier-Code-27B-MLX-4bit")
    
    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 Outlier-Ai/Outlier-Code-27B-MLX-4bit with Pi:

    Start the MLX server
    # Install MLX LM:
    uv tool install mlx-lm
    # Start a local OpenAI-compatible server:
    mlx_lm.server --model "Outlier-Ai/Outlier-Code-27B-MLX-4bit"
    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": "Outlier-Ai/Outlier-Code-27B-MLX-4bit"
            }
          ]
        }
      }
    }
    Run Pi
    # Start Pi in your project directory:
    pi
  • MLX LM

    How to use Outlier-Ai/Outlier-Code-27B-MLX-4bit with MLX LM:

    Generate or start a chat session
    # Install MLX LM
    uv tool install mlx-lm
    # Interactive chat REPL
    mlx_lm.chat --model "Outlier-Ai/Outlier-Code-27B-MLX-4bit"
    Run an OpenAI-compatible server
    # Install MLX LM
    uv tool install mlx-lm
    # Start the server
    mlx_lm.server --model "Outlier-Ai/Outlier-Code-27B-MLX-4bit"
    # Calling the OpenAI-compatible server with curl
    curl -X POST "http://localhost:8000/v1/chat/completions" \
       -H "Content-Type: application/json" \
       --data '{
         "model": "Outlier-Ai/Outlier-Code-27B-MLX-4bit",
         "messages": [
           {"role": "user", "content": "Hello"}
         ]
       }'
Outlier-Code-27B-MLX-4bit
15.2 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 7 commits
ur-dad-matt's picture
ur-dad-matt
docs(card): SEO + cross-link refresh (HF-DISCOVERABILITY-001)
0297ea8 verified 6 days ago
  • .gitattributes
    1.57 kB
    feat: v1.6 path-B code weights (text-only stripped) 11 days ago
  • README.md
    3.53 kB
    docs(card): SEO + cross-link refresh (HF-DISCOVERABILITY-001) 6 days ago
  • chat_template.jinja
    7.76 kB
    feat: v1.6 path-B code weights (text-only stripped) 11 days ago
  • config.json
    5.99 kB
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  • configuration.json
    51 Bytes
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  • generation_config.json
    377 Bytes
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  • model-00001-of-00003.safetensors
    4.42 GB
    xet
    feat: v1.6 path-B code weights (text-only stripped) 11 days ago
  • model-00002-of-00003.safetensors
    5.35 GB
    xet
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  • model-00003-of-00003.safetensors
    5.36 GB
    xet
    feat: v1.6 path-B code weights (text-only stripped) 11 days ago
  • model.safetensors.index.json
    182 kB
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  • tokenizer.json
    20 MB
    xet
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  • tokenizer_config.json
    1.14 kB
    feat: v1.6 path-B code weights (text-only stripped) 11 days ago
  • vocab.json
    6.72 MB
    feat: v1.6 path-B code weights (text-only stripped) 11 days ago