Instructions to use MCES10-Software/cpp-qwen3-4B-Instruct-2507 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MCES10-Software/cpp-qwen3-4B-Instruct-2507 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("MCES10-Software/cpp-qwen3-4B-Instruct-2507")

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)

Inference
Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

Pi new

How to use MCES10-Software/cpp-qwen3-4B-Instruct-2507 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "MCES10-Software/cpp-qwen3-4B-Instruct-2507"

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": "MCES10-Software/cpp-qwen3-4B-Instruct-2507"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MCES10-Software/cpp-qwen3-4B-Instruct-2507 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 "MCES10-Software/cpp-qwen3-4B-Instruct-2507"

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 MCES10-Software/cpp-qwen3-4B-Instruct-2507

Run Hermes

hermes

MLX LM

How to use MCES10-Software/cpp-qwen3-4B-Instruct-2507 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "MCES10-Software/cpp-qwen3-4B-Instruct-2507"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "MCES10-Software/cpp-qwen3-4B-Instruct-2507"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "MCES10-Software/cpp-qwen3-4B-Instruct-2507",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

MCES10 commited on Sep 5, 2025

Commit

295a6a9

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1 Parent(s): 5ba457f

Update README.md

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README.md +44 -0

README.md CHANGED Viewed

@@ -6,4 +6,48 @@ pipeline_tag: text-generation
 base_model: Qwen/Qwen3-4B-Instruct-2507
 tags:
 - mlx
 ---

 base_model: Qwen/Qwen3-4B-Instruct-2507
 tags:
 - mlx
+- code
+datasets:
+- MCES10-Software/CPP-Code-Solutions
+language:
+- en
 ---
+<center>
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/65a17a03b172f47c9c31eab9/GY9R6DnA_TGzefzmq4HRi.png" width="200" height="200">
+</center>
+# CPP-qwen3-4B-Instruct-2507
+## Features
+- This model is based on qwen3-4B-Instruct-2507
+- Fine Tuned on MCES10-Software/CPP-Code-Solutions Dataset
+- 4 Billion Parameters
+- Finetuned with MLX
+- model.safetensors
+## Benchmark
+MAX TOKENS = 500
+Apple Silicon Macbook Pro 18,3 M1 PRO 16 GB RAM
+Write a function that checks if a number is prime.
+```cpp
+bool isPrime(int n) {
+    if (n <= 1) return false;
+    for (int i = 2; i * i <= n; ++i) {
+        if (n % i == 0) return false;
+    }
+    return true;
+}
+```
+49.27 tok/sec
+63 tokens
+0.24s to first token
+Credits
+MCES10 Software
+Thanks to:
+QWEN for the model