Instructions to use dalatexcoder/MiniCPM5-2B-5BitL-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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"

dalatexcoder commited on 10 days ago

Commit

8be25e0

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@@ -123,31 +123,31 @@ Quantized from [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B)
       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

       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