Instructions to use pierretokns/qwen-0.5b-ccmcp-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pierretokns/qwen-0.5b-ccmcp-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pierretokns/qwen-0.5b-ccmcp-v2")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("pierretokns/qwen-0.5b-ccmcp-v2", device_map="auto")

MLX

How to use pierretokns/qwen-0.5b-ccmcp-v2 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("pierretokns/qwen-0.5b-ccmcp-v2")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use pierretokns/qwen-0.5b-ccmcp-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pierretokns/qwen-0.5b-ccmcp-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pierretokns/qwen-0.5b-ccmcp-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pierretokns/qwen-0.5b-ccmcp-v2

SGLang

How to use pierretokns/qwen-0.5b-ccmcp-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pierretokns/qwen-0.5b-ccmcp-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pierretokns/qwen-0.5b-ccmcp-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pierretokns/qwen-0.5b-ccmcp-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pierretokns/qwen-0.5b-ccmcp-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

MLX LM

How to use pierretokns/qwen-0.5b-ccmcp-v2 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "pierretokns/qwen-0.5b-ccmcp-v2" --prompt "Once upon a time"

Docker Model Runner
How to use pierretokns/qwen-0.5b-ccmcp-v2 with Docker Model Runner:
```
docker model run hf.co/pierretokns/qwen-0.5b-ccmcp-v2
```

pierretokns commited on Jan 27

Commit

dab55dd

verified ·

1 Parent(s): 49dc7f7

Upload adapter_config.json with huggingface_hub

Browse files

Files changed (1) hide show

adapter_config.json +40 -0

adapter_config.json ADDED Viewed

	@@ -0,0 +1,40 @@

+{
+    "adapter_path": "models/qwen-0.5b-ccmcp-v2-lora",
+    "batch_size": 1,
+    "config": null,
+    "data": "data/mcp_training_text",
+    "fine_tune_type": "lora",
+    "grad_accumulation_steps": 1,
+    "grad_checkpoint": true,
+    "iters": 2000,
+    "learning_rate": 5e-05,
+    "lora_parameters": {
+        "rank": 8,
+        "dropout": 0.0,
+        "scale": 20.0
+    },
+    "lr_schedule": null,
+    "mask_prompt": false,
+    "max_seq_length": 2048,
+    "model": "mlx-community/Qwen2.5-Coder-0.5B-Instruct-4bit",
+    "num_layers": 16,
+    "optimizer": "adam",
+    "optimizer_config": {
+        "adam": {},
+        "adamw": {},
+        "muon": {},
+        "sgd": {},
+        "adafactor": {}
+    },
+    "project_name": null,
+    "report_to": null,
+    "resume_adapter_file": null,
+    "save_every": 500,
+    "seed": 0,
+    "steps_per_eval": 200,
+    "steps_per_report": 100,
+    "test": false,
+    "test_batches": 500,
+    "train": true,
+    "val_batches": 25
+}