Text Generation
MLX
Safetensors
English
Chinese
llama
minicpm
minicpm5
long-context
tool-calling
on-device
edge-ai
conversational
4-bit precision
Instructions to use dalatexcoder/MiniCPM5-2B-5BitL-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use dalatexcoder/MiniCPM5-2B-5BitL-MLX 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("dalatexcoder/MiniCPM5-2B-5BitL-MLX") 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 Settings
- LM Studio
- Pi
How to use dalatexcoder/MiniCPM5-2B-5BitL-MLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "dalatexcoder/MiniCPM5-2B-5BitL-MLX"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "dalatexcoder/MiniCPM5-2B-5BitL-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use dalatexcoder/MiniCPM5-2B-5BitL-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "dalatexcoder/MiniCPM5-2B-5BitL-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "dalatexcoder/MiniCPM5-2B-5BitL-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dalatexcoder/MiniCPM5-2B-5BitL-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use dalatexcoder/MiniCPM5-2B-5BitL-MLX 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 "dalatexcoder/MiniCPM5-2B-5BitL-MLX"
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 dalatexcoder/MiniCPM5-2B-5BitL-MLX
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use dalatexcoder/MiniCPM5-2B-5BitL-MLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "dalatexcoder/MiniCPM5-2B-5BitL-MLX"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "dalatexcoder/MiniCPM5-2B-5BitL-MLX" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
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Download README.md from dalatexcoder/MiniCPM5-2B-5BitL-MLX: direct link, hf CLI and curl.
- Browser
- Download file 13.6 kB
-
https://huggingface.co/dalatexcoder/MiniCPM5-2B-5BitL-MLX/resolve/main/README.md
- Command line
-
hf download hf://dalatexcoder/MiniCPM5-2B-5BitL-MLX/README.md
-
curl -L -o README.md https://huggingface.co/dalatexcoder/MiniCPM5-2B-5BitL-MLX/resolve/main/README.md
13.6 kB
| license: apache-2.0 | |
| language: | |
| - en | |
| - zh | |
| library_name: mlx | |
| pipeline_tag: text-generation | |
| tags: | |
| - minicpm | |
| - minicpm5 | |
| - llama | |
| - text-generation | |
| - long-context | |
| - tool-calling | |
| - on-device | |
| - edge-ai | |
| - mlx | |
| datasets: | |
| - openbmb/Ultra-FineWeb | |
| - openbmb/UltraX-Preview | |
| - openbmb/Ultra-FineWeb-L3 | |
| - openbmb/UltraData-Math | |
| - openbmb/UltraData-Code | |
| - openbmb/UltraData-SFT-2605 | |
| - openbmb/UltraData-SFT-Agent-2609 | |
| - openbmb/UltraData-RL-2609 | |
| base_model: openbmb/MiniCPM5-2B | |
| Quantized from [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B). Effective average BPW is around 5.669. | |
| Top 20 most-sensitive layers at 2-bit: | |
| 1. model.layers.1.mlp.down_proj sens=1.002e+00 params=12,582,912 | |
| 2. model.layers.7.mlp.down_proj sens=4.134e-01 params=12,582,912 | |
| 3. lm_head sens=3.690e-01 params=267,386,880 | |
| 4. model.layers.1.self_attn.q_proj sens=2.767e-01 params=4,194,304 | |
| 5. model.layers.0.self_attn.v_proj sens=1.715e-01 params=524,288 | |
| 6. model.layers.41.mlp.down_proj sens=1.299e-01 params=12,582,912 | |
| 7. model.layers.1.self_attn.o_proj sens=1.231e-01 params=4,194,304 | |
| 8. model.layers.4.self_attn.v_proj sens=1.165e-01 params=524,288 | |
| 9. model.layers.2.self_attn.q_proj sens=9.487e-02 params=4,194,304 | |
| 10. model.layers.2.self_attn.v_proj sens=9.286e-02 params=524,288 | |
| 11. model.layers.0.mlp.down_proj sens=8.109e-02 params=12,582,912 | |
| 12. model.layers.13.self_attn.o_proj sens=7.899e-02 params=4,194,304 | |
| 13. model.layers.14.self_attn.o_proj sens=7.754e-02 params=4,194,304 | |
| 14. model.layers.3.self_attn.v_proj sens=7.545e-02 params=524,288 | |
| 15. model.layers.16.self_attn.o_proj sens=7.483e-02 params=4,194,304 | |
| 16. model.layers.21.self_attn.o_proj sens=7.397e-02 params=4,194,304 | |
| 17. model.layers.1.self_attn.v_proj sens=7.267e-02 params=524,288 | |
| 18. model.layers.5.self_attn.q_proj sens=6.835e-02 params=4,194,304 | |
| 19. model.layers.5.self_attn.v_proj sens=6.390e-02 params=524,288 | |
| 20. model.layers.6.mlp.down_proj sens=6.340e-02 params=12,582,912 | |
| Top 20 most-sensitive layers at 3-bit: | |
| 1. model.layers.7.mlp.down_proj sens=1.488e-01 params=12,582,912 | |
| 2. model.layers.1.mlp.down_proj sens=8.359e-02 params=12,582,912 | |
| 3. lm_head sens=7.595e-02 params=267,386,880 | |
| 4. model.layers.2.self_attn.v_proj sens=4.404e-02 params=524,288 | |
| 5. model.layers.0.self_attn.v_proj sens=4.262e-02 params=524,288 | |
| 6. model.layers.3.self_attn.v_proj sens=3.825e-02 params=524,288 | |
| 7. model.layers.2.self_attn.q_proj sens=3.742e-02 params=4,194,304 | |
| 8. model.layers.1.self_attn.o_proj sens=3.118e-02 params=4,194,304 | |
| 9. model.layers.41.mlp.down_proj sens=2.823e-02 params=12,582,912 | |
| 10. model.layers.1.self_attn.q_proj sens=2.587e-02 params=4,194,304 | |
| 11. model.layers.5.self_attn.v_proj sens=2.548e-02 params=524,288 | |
| 12. model.layers.4.self_attn.v_proj sens=2.526e-02 params=524,288 | |
| 13. model.layers.1.self_attn.v_proj sens=2.486e-02 params=524,288 | |
| 14. model.layers.5.self_attn.q_proj sens=2.426e-02 params=4,194,304 | |
| 15. model.layers.14.self_attn.o_proj sens=2.276e-02 params=4,194,304 | |
| 16. model.layers.12.self_attn.v_proj sens=2.275e-02 params=524,288 | |
| 17. model.layers.0.mlp.down_proj sens=2.242e-02 params=12,582,912 | |
| 18. model.layers.13.self_attn.o_proj sens=2.229e-02 params=4,194,304 | |
| 19. model.layers.6.mlp.down_proj sens=2.124e-02 params=12,582,912 | |
| 20. model.layers.16.self_attn.o_proj sens=2.064e-02 params=4,194,304 | |
| Top 20 most-sensitive layers at 4-bit: | |
| 1. model.layers.7.mlp.down_proj sens=4.436e-02 params=12,582,912 | |
| 2. model.layers.1.mlp.down_proj sens=2.787e-02 params=12,582,912 | |
| 3. model.layers.0.self_attn.v_proj sens=2.504e-02 params=524,288 | |
| 4. lm_head sens=1.819e-02 params=267,386,880 | |
| 5. model.layers.2.self_attn.v_proj sens=1.638e-02 params=524,288 | |
| 6. model.layers.5.self_attn.q_proj sens=1.346e-02 params=4,194,304 | |
| 7. model.layers.1.self_attn.o_proj sens=1.188e-02 params=4,194,304 | |
| 8. model.layers.4.self_attn.v_proj sens=1.160e-02 params=524,288 | |
| 9. model.layers.1.self_attn.q_proj sens=1.062e-02 params=4,194,304 | |
| 10. model.layers.2.self_attn.o_proj sens=1.054e-02 params=4,194,304 | |
| 11. model.layers.3.self_attn.v_proj sens=9.660e-03 params=524,288 | |
| 12. model.layers.2.self_attn.q_proj sens=9.620e-03 params=4,194,304 | |
| 13. model.layers.10.mlp.up_proj sens=9.215e-03 params=12,582,912 | |
| 14. model.layers.0.self_attn.o_proj sens=9.097e-03 params=4,194,304 | |
| 15. model.layers.1.self_attn.v_proj sens=9.091e-03 params=524,288 | |
| 16. model.layers.0.mlp.down_proj sens=8.764e-03 params=12,582,912 | |
| 17. model.layers.5.self_attn.v_proj sens=8.714e-03 params=524,288 | |
| 18. model.layers.9.self_attn.v_proj sens=8.605e-03 params=524,288 | |
| 19. model.layers.14.self_attn.o_proj sens=7.816e-03 params=4,194,304 | |
| 20. model.layers.4.mlp.up_proj sens=7.747e-03 params=12,582,912 | |
| Top 20 most-sensitive layers at 5-bit: | |
| 1. model.layers.7.mlp.down_proj sens=1.577e-02 params=12,582,912 | |
| 2. model.layers.1.mlp.down_proj sens=9.463e-03 params=12,582,912 | |
| 3. model.layers.0.self_attn.v_proj sens=7.656e-03 params=524,288 | |
| 4. model.layers.2.self_attn.v_proj sens=6.293e-03 params=524,288 | |
| 5. model.layers.1.self_attn.o_proj sens=6.019e-03 params=4,194,304 | |
| 6. model.layers.5.mlp.up_proj sens=5.718e-03 params=12,582,912 | |
| 7. model.layers.1.self_attn.q_proj sens=5.607e-03 params=4,194,304 | |
| 8. model.layers.3.self_attn.v_proj sens=5.563e-03 params=524,288 | |
| 9. model.layers.4.mlp.down_proj sens=5.433e-03 params=12,582,912 | |
| 10. model.layers.2.mlp.gate_proj sens=5.274e-03 params=12,582,912 | |
| 11. model.layers.0.mlp.down_proj sens=5.241e-03 params=12,582,912 | |
| 12. model.layers.2.self_attn.o_proj sens=5.067e-03 params=4,194,304 | |
| 13. model.layers.2.self_attn.q_proj sens=5.060e-03 params=4,194,304 | |
| 14. model.layers.4.mlp.up_proj sens=4.983e-03 params=12,582,912 | |
| 15. model.layers.1.self_attn.v_proj sens=4.959e-03 params=524,288 | |
| 16. model.layers.0.self_attn.o_proj sens=4.954e-03 params=4,194,304 | |
| 17. model.layers.5.self_attn.v_proj sens=4.817e-03 params=524,288 | |
| 18. model.layers.0.mlp.gate_proj sens=4.768e-03 params=12,582,912 | |
| 19. lm_head sens=4.615e-03 params=267,386,880 | |
| 20. model.layers.2.mlp.up_proj sens=4.561e-03 params=12,582,912 | |
| Top 20 most-sensitive layers at 6-bit: | |
| 1. model.layers.7.mlp.down_proj sens=6.492e-03 params=12,582,912 | |
| 2. model.layers.1.mlp.down_proj sens=6.159e-03 params=12,582,912 | |
| 3. model.layers.0.self_attn.v_proj sens=5.739e-03 params=524,288 | |
| 4. model.layers.3.self_attn.k_proj sens=5.012e-03 params=524,288 | |
| 5. model.layers.2.self_attn.v_proj sens=4.739e-03 params=524,288 | |
| 6. model.layers.0.mlp.up_proj sens=4.594e-03 params=12,582,912 | |
| 7. model.layers.2.self_attn.q_proj sens=4.409e-03 params=4,194,304 | |
| 8. model.layers.2.mlp.down_proj sens=4.399e-03 params=12,582,912 | |
| 9. model.layers.6.mlp.up_proj sens=4.348e-03 params=12,582,912 | |
| 10. model.layers.1.self_attn.v_proj sens=4.302e-03 params=524,288 | |
| 11. model.layers.2.self_attn.o_proj sens=4.259e-03 params=4,194,304 | |
| 12. model.layers.4.self_attn.q_proj sens=4.223e-03 params=4,194,304 | |
| 13. model.layers.1.mlp.gate_proj sens=4.110e-03 params=12,582,912 | |
| 14. model.layers.2.self_attn.k_proj sens=4.094e-03 params=524,288 | |
| 15. model.layers.6.self_attn.q_proj sens=3.977e-03 params=4,194,304 | |
| 16. model.layers.6.mlp.gate_proj sens=3.973e-03 params=12,582,912 | |
| 17. model.layers.1.self_attn.o_proj sens=3.940e-03 params=4,194,304 | |
| 18. model.layers.4.mlp.down_proj sens=3.883e-03 params=12,582,912 | |
| 19. model.layers.5.mlp.gate_proj sens=3.870e-03 params=12,582,912 | |
| 20. model.layers.0.self_attn.o_proj sens=3.824e-03 params=4,194,304 | |
| Top 20 most-sensitive layers at 8-bit: | |
| 1. model.layers.1.self_attn.v_proj sens=4.394e-03 params=524,288 | |
| 2. model.layers.2.self_attn.v_proj sens=4.088e-03 params=524,288 | |
| 3. model.layers.4.self_attn.k_proj sens=3.958e-03 params=524,288 | |
| 4. model.layers.1.mlp.gate_proj sens=3.900e-03 params=12,582,912 | |
| 5. model.layers.0.mlp.up_proj sens=3.824e-03 params=12,582,912 | |
| 6. model.layers.0.mlp.down_proj sens=3.799e-03 params=12,582,912 | |
| 7. model.layers.0.self_attn.k_proj sens=3.778e-03 params=524,288 | |
| 8. model.layers.0.self_attn.o_proj sens=3.697e-03 params=4,194,304 | |
| 9. model.layers.4.mlp.up_proj sens=3.644e-03 params=12,582,912 | |
| 10. model.layers.0.self_attn.v_proj sens=3.592e-03 params=524,288 | |
| 11. model.layers.4.mlp.gate_proj sens=3.513e-03 params=12,582,912 | |
| 12. model.layers.5.self_attn.v_proj sens=3.422e-03 params=524,288 | |
| 13. model.layers.1.mlp.up_proj sens=3.421e-03 params=12,582,912 | |
| 14. model.layers.3.self_attn.q_proj sens=3.398e-03 params=4,194,304 | |
| 15. model.layers.6.mlp.gate_proj sens=3.394e-03 params=12,582,912 | |
| 16. model.layers.4.mlp.down_proj sens=3.328e-03 params=12,582,912 | |
| 17. model.layers.5.self_attn.q_proj sens=3.321e-03 params=4,194,304 | |
| 18. model.layers.0.self_attn.q_proj sens=3.279e-03 params=4,194,304 | |
| 19. model.layers.2.mlp.gate_proj sens=3.275e-03 params=12,582,912 | |
| 20. model.layers.0.mlp.gate_proj sens=3.252e-03 params=12,582,912 | |
| Optimization result: 34 @ 3-bit, 95 @ 4-bit, 84 @ 5-bit, 42 @ 6-bit, 40 @ 8-bit |