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

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 / README.md

Walid Sobhi

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	---
	base_model: Qwen/Qwen2.5-Coder-3B-Instruct
	datasets:
	- my-ai-stack/Stack-4.0-Dataset
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- qwen2.5-coder
	- qwen2.5-coder-3b
	- code-generation
	- agentic-ai
	- tool-use
	- fine-tuned-llm
	- stack-4
	- stack-ai
	- sovereign-ai
	- enterprise
	- local-inference
	- 3b-parameter-model
	model-index:
	- name: Stack 4.0 Omni-Nexus Merged
	results:
	- task:
	type: text-generation
	description: HellaSwag commonsense reasoning
	dataset:
	name: HellaSwag
	type: hellaswag
	metrics:
	- type: acc_norm
	value: 74.0%
	- task:
	type: text-generation
	description: ARC-Challenge reasoning
	dataset:
	name: ARC-Challenge
	type: ai2_arc
	metrics:
	- type: acc_norm
	value: 52.0%
	---

	<div style="background-color: #030406; padding: 60px 40px; border-radius: 40px 40px 0 0; border: 1px solid #111827; border-bottom: none; font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; color: #ffffff; text-align: center; position: relative; overflow: hidden;">

	<div style="position: absolute; top: -100px; left: -100px; width: 400px; height: 400px; background: radial-gradient(circle, rgba(219, 39, 119, 0.08) 0%, transparent 70%);"></div>

	<div style="margin: 0 auto 30px; width: 80px; height: 60px; position: relative;">
	<div style="position: absolute; width: 100%; height: 18px; background: linear-gradient(135deg, #c084fc 0%, #db2777 100%); border-radius: 6px; top: 0px; z-index: 3; box-shadow: 0 10px 20px rgba(0,0,0,0.5); border-bottom: 2px solid rgba(0,0,0,0.2);"></div>
	<div style="position: absolute; width: 100%; height: 18px; background: linear-gradient(135deg, #c084fc 0%, #db2777 100%); border-radius: 6px; top: 22px; z-index: 2; opacity: 0.7; border-bottom: 2px solid rgba(0,0,0,0.2);"></div>
	<div style="position: absolute; width: 100%; height: 18px; background: linear-gradient(135deg, #c084fc 0%, #db2777 100%); border-radius: 6px; top: 44px; z-index: 1; opacity: 0.4; border-bottom: 2px solid rgba(0,0,0,0.2);"></div>
	</div>

	<h1 style="background: linear-gradient(135deg, #ffffff 0%, #a1a1aa 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent; font-size: 3rem; letter-spacing: -1.5px; margin: 10px 0; font-weight: 800;">Stack 4.0 Omni-Nexus</h1>

	<p style="color: #db2777; font-weight: 600; letter-spacing: 3px; text-transform: uppercase; font-size: 0.85rem; margin-bottom: 30px; opacity: 0.9;">Merged · 3B Parameters · Sovereign Agentic Infrastructure</p>

	<div align="center" style="display: flex; justify-content: center; gap: 10px; flex-wrap: wrap;">
	<img src="https://img.shields.io/badge/Release-v4.0_Alpha-db2777?style=for-the-badge" alt="Version">
	<img src="https://img.shields.io/badge/Network-Global-111827?style=for-the-badge&border=db2777" alt="Network">
	<img src="https://img.shields.io/badge/Security-Sovereign-c084fc?style=for-the-badge" alt="Security">
	</div>

	<div style="height: 1px; width: 100%; background: linear-gradient(to right, transparent, #111827, #db2777, #111827, transparent); margin-top: 50px; opacity: 0.5;"></div>
	</div>

	---

	# Stack 4.0 Omni-Nexus — Merged

	Model ID: `my-ai-stack/Stack-4.0-Qwen-3B-Merged`

	A 3-billion parameter instruction-tuned coding model, fully merged from Qwen2.5-Coder-3B-Instruct with 55,000 agentic tool-use conversations baked in. This is the standalone version — no adapter needed, runs directly on any compatible hardware.

	## Performance Benchmarks

	\| Benchmark \| Score \| Notes \|
	\|-----------\|-------\|-------\|
	\| HellaSwag (acc_norm) \| 74.0% \| 50-sample eval \|
	\| ARC-Challenge (acc_norm) \| 52.0% \| 50-sample eval \|
	\| Internal coding sample \| 10/10 \| All valid Python produced \|

	## Key Metrics

	\| Metric \| Value \|
	\|--------\|-------\|
	\| Parameters \| 3B \|
	\| Training loss (final) \| 0.1411 \|
	\| Training steps \| 1,000 \|
	\| Hardware \| GCP Tesla V100 16GB \|
	\| Training time \| ~10 hours \|

	## Why Merged?

	The merged version ships the full model in a single file — no LoRA adapters, no base model dependency. Deploy anywhere that supports Hugging Face Transformers.

	## Usage

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	MODEL = "my-ai-stack/Stack-4.0-Qwen-3B-Merged"

	tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
	tokenizer.pad_token = tokenizer.eos_token

	model = AutoModelForCausalLM.from_pretrained(
	MODEL, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
	)
	model.eval()

	messages = [{"role": "user", "content": "Write a quicksort in Python"}]
	text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = tokenizer(text, return_tensors="pt").to(model.device)

	with torch.no_grad():
	out = model.generate(**inputs, max_new_tokens=512, temperature=0.7)

	print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	## Training Details

	\| Parameter \| Value \|
	\|-----------\|-------\|
	\| Method \| QLoRA → Merged \|
	\| LoRA rank \| 16 \|
	\| Trainable params \| 7.3M / 3.1B (0.24%) \|
	\| Batch size \| 1 \|
	\| Grad accumulation \| 16 \|
	\| Max length \| 512 \|
	\| Learning rate \| 2e-4 \|
	\| Optimizer \| AdamW (bf16) \|
	\| Hardware \| GCP V100 16GB \|

	## Limitations

	- 3B model — smaller than 7B models; less capable on complex multi-step reasoning
	- English-optimized — other language performance may vary
	- Tool execution — tool calls are generated but actual execution requires an agent loop in your application

	## See Also

	- [LoRA Adapter version](https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic) — smaller, needs base model
	- [Training dataset](https://huggingface.co/my-ai-stack/Stack-4.0-Dataset)
	- [Stack 3.0 (7B)](https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus)

	## Citation

	```bibtex
	@misc{stack-4-merged-2026,
	title={Stack 4.0 Omni-Nexus — Merged},
	author={Stack AI Team},
	year={2026},
	url={https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Merged}
	}
	```