Instructions to use my-ai-stack/Stack-4.0-Qwen-3B-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use my-ai-stack/Stack-4.0-Qwen-3B-Merged with Transformers:

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

pipe = pipeline("text-generation", model="my-ai-stack/Stack-4.0-Qwen-3B-Merged")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-4.0-Qwen-3B-Merged")
model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-4.0-Qwen-3B-Merged")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use my-ai-stack/Stack-4.0-Qwen-3B-Merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "my-ai-stack/Stack-4.0-Qwen-3B-Merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-4.0-Qwen-3B-Merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/my-ai-stack/Stack-4.0-Qwen-3B-Merged

SGLang

How to use my-ai-stack/Stack-4.0-Qwen-3B-Merged 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 "my-ai-stack/Stack-4.0-Qwen-3B-Merged" \
    --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": "my-ai-stack/Stack-4.0-Qwen-3B-Merged",
		"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 "my-ai-stack/Stack-4.0-Qwen-3B-Merged" \
        --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": "my-ai-stack/Stack-4.0-Qwen-3B-Merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use my-ai-stack/Stack-4.0-Qwen-3B-Merged with Docker Model Runner:
```
docker model run hf.co/my-ai-stack/Stack-4.0-Qwen-3B-Merged
```

Stack-4.0-Qwen-3B-Merged / benchmark_results.json

Walid Sobhi

Upload benchmark_results.json with huggingface_hub

59689ba verified about 1 month ago

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	{
	"model": "my-ai-stack/Stack-4.0-Qwen-3B-Merged",
	"date": "2026-04-26",
	"hardware": "GCP Tesla V100 16GB",
	"training": {
	"final_loss": 0.1411,
	"total_steps": 1000,
	"effective_batch_size": 16,
	"learning_rate": 0.0002,
	"method": "QLoRA \u2192 Merged",
	"trainable_params": "7.3M / 3.1B (0.24%)",
	"training_time": "~10 hours",
	"cost": " GCP spot instance"
	},
	"benchmarks": {
	"hellaswag": {
	"acc_norm": 0.74,
	"acc": 0.52
	},
	"arc_challenge": {
	"acc_norm": 0.52,
	"acc": 0.48
	}
	},
	"coding_sample": "10/10 valid Python"
	}