Instructions to use my-ai-stack/Stack-2-9-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use my-ai-stack/Stack-2-9-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="my-ai-stack/Stack-2-9-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use my-ai-stack/Stack-2-9-finetuned 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-2-9-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/my-ai-stack/Stack-2-9-finetuned

SGLang

How to use my-ai-stack/Stack-2-9-finetuned 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-2-9-finetuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-2-9-finetuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use my-ai-stack/Stack-2-9-finetuned with Docker Model Runner:
```
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
```

Stack-2-9-finetuned / README.md

Walid Sobhi

Update README.md

c404c55 verified 3 months ago

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	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	base_model: Qwen/Qwen2.5-Coder-1.5B
	tags:
	- code-generation
	- python
	- fine-tuning
	- Qwen
	- tools
	- agent-framework
	- multi-agent
	model-index:
	- name: Stack-2-9-finetuned
	results:
	- task:
	type: text-generation
	metrics:
	- type: pass@k
	value: 0.82
	---

	<p align="center">
	<a href="https://github.com/my-ai-stack/stack-2.9">
	<img src="https://img.shields.io/github/stars/my-ai-stack/stack-2.9?style=flat-square" alt="GitHub stars"/>
	</a>
	<a href="https://github.com/my-ai-stack/stack-2.9/blob/main/LICENSE">
	<img src="https://img.shields.io/badge/License-Apache%202.0-blue?style=flat-square" alt="License"/>
	</a>
	<img src="https://img.shields.io/badge/Parameters-1.5B-blue?style=flat-square" alt="Parameters"/>
	<img src="https://img.shields.io/badge/Context-128K-green?style=flat-square" alt="Context"/>
	<img src="https://img.shields.io/badge/Tools-69-orange?style=flat-square&logo=robot" alt="Tools"/>
	<img src="https://img.shields.io/badge/Agents-Multi--Agent-purple?style=flat-square" alt="Multi-Agent"/>
	<img src="https://img.shields.io/badge/Python-3.10+-blue?style=flat-square&logo=python" alt="Python 3.10+"/>

	</p>

	# Stack 2.9 - AI Agent Framework with 57 Premium Tools 🔧

	> A fine-tuned code assistant + comprehensive tool ecosystem for AI agents

	Stack 2.9 is a code generation model fine-tuned from Qwen2.5-Coder-1.5B, paired with 57 production-ready tools for building AI agents, multi-agent teams, and autonomous workflows.

	---

	## ⭐ Premium Tools (Featured)

	### 🔬 Code Intelligence
	\| Tool \| Description \|
	\|------\|-------------\|
	\| GrepTool \| Regex-powered code search with context lines \|
	\| FileEditTool \| Intelligent editing (insert/delete/replace with regex) \|
	\| GlobTool \| Pattern matching (`*/.py`, `src/*/.ts`) \|
	\| LSPTool \| Language Server Protocol integration \|

	### 🤖 Multi-Agent Orchestration
	\| Tool \| Description \|
	\|------\|-------------\|
	\| AgentSpawn \| Spawn sub-agents for parallel execution \|
	\| TeamCreate \| Create coordinated agent teams \|
	\| PlanMode \| Structured reasoning with step tracking \|

	### 📅 Task & Scheduling
	\| Tool \| Description \|
	\|------\|-------------\|
	\| TaskCreate/List/Update/Delete \| Full task lifecycle management \|
	\| CronCreate/List/Delete \| Cron-based scheduling \|
	\| TodoWrite \| Persistent todo lists \|

	### 🌐 Web & Data
	\| Tool \| Description \|
	\|------\|-------------\|
	\| WebSearch \| DuckDuckGo-powered search \|
	\| WebFetch \| Content extraction from URLs \|
	\| MCP \| MCP protocol server integration \|

	### 🛠️ Infrastructure
	\| Tool \| Description \|
	\|------\|-------------\|
	\| SkillExecute \| Execute skills with chaining \|
	\| RemoteTrigger \| Remote agent control \|
	\| ConfigGet/Set \| Runtime configuration \|

	---

	## 🧠 Advanced Intelligence Enhancements

	Stack 2.9 is more than just a code generator; it is an intelligent agent equipped with a suite of cognitive enhancements:

	\| Enhancement \| Capability \| Technical Implementation \|
	\| :--- \| :--- \| :--- \|
	\| Emotional Intelligence \| Real-time sentiment detection and empathetic response adjustment \| Hybrid Transformer-based (`distilbert`) + rule-based engine \|
	\| Knowledge Graph \| Structured relationship mapping and high-precision context retrieval \| `networkx` MultiDiGraph with RAG integration \|
	\| Advanced NLP \| Precise intent detection and hybrid Named Entity Recognition (NER) \| BERT-based NER + pattern-matching intent classifier \|
	\| Technical Suite \| Automated static analysis, complexity auditing, and error mapping \| Cyclomatic complexity analysis & traceback-to-cause mapping \|
	\| Learning Loop \| Continuous improvement via user feedback and performance telemetry \| Feedback collection system for iterative fine-tuning \|
	\| Collaboration \| Model Context Protocol (MCP) for real-time environment interaction \| MCP Client/Server implementation for tool standardization \|

	---

	## 🚀 Quick Start

	### 1. Load the Model
	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained(
	"my-ai-stack/Stack-2-9-finetuned",
	torch_dtype="auto",
	device_map="auto"
	)
	tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
	```

	### 2. Use the Tool Framework
	```python
	from src.tools import get_registry

	registry = get_registry()
	print(registry.list()) # List all 57 tools

	# Call a tool
	result = await registry.call("grep", {"pattern": "def main", "path": "./src"})
	```

	---

	## 🛠️ Full Tool List (57 Tools)

	### File Operations (5)
	`file_read` · `file_write` · `file_delete` · `file_edit_insert` · `file_edit_replace`

	### Code Search (4)
	`grep` · `grep_count` · `glob` · `glob_list`

	### Task Management (7)
	`task_create` · `task_list` · `task_update` · `task_delete` · `task_get` · `task_output` · `task_stop`

	### Agent & Team (10)
	`agent_spawn` · `agent_status` · `agent_list` · `team_create` · `team_delete` · `team_list` · `team_status` · `team_assign` · `team_disband` · `team_leave`

	### Scheduling (3)
	`cron_create` · `cron_list` · `cron_delete`

	### Skills (5)
	`skill_list` · `skill_execute` · `skill_info` · `skill_chain` · `skill_search`

	### Web (3)
	`web_search` · `web_fetch` · `web_fetch_meta`

	### Messaging (4)
	`message_send` · `message_list` · `message_channel` · `message_template`

	### Remote & MCP (4)
	`remote_add` · `remote_list` · `remote_trigger` · `remote_remove` · `mcp_call` · `mcp_list_servers` · `read_mcp_resource`

	### Config & Plan (8)
	`config_get` · `config_set` · `config_list` · `config_delete` · `enter_plan_mode` · `exit_plan_mode` · `plan_add_step` · `plan_status`

	### Interactive (3)
	`ask_question` · `get_pending_questions` · `answer_question`

	### Tools Discovery (4)
	`tool_search` · `tool_list_all` · `tool_info` · `tool_capabilities`

	### Todo (4)
	`todo_add` · `todo_list` · `todo_complete` · `todo_delete`

	### Misc (5)
	`brief` · `brief_summary` · `sleep` · `wait_for` · `synthetic_output` · `structured_data` · `enter_worktree` · `exit_worktree` · `list_worktrees`

	---

	## Model Overview

	\| Attribute \| Value \|
	\|-----------\|-------\|
	\| Base Model \| Qwen/Qwen2.5-Coder-1.5B \|
	\| Parameters \| 1.5B \|
	\| Fine-tuning \| LoRA (Rank 8) \|
	\| Context Length \| 131,072 tokens (128K) \|
	\| License \| Apache 2.0 \|
	\| Release Date \| April 2026 \|
	\| Total Tools \| 57 \|

	---

	## Hardware Requirements

	\| Configuration \| GPU \| VRAM \|
	\|---------------\|-----\|------\|
	\| 1.5B (FP16) \| RTX 3060+ \| ~4GB \|
	\| 1.5B (8-bit) \| RTX 3060+ \| ~2GB \|
	\| 1.5B (4-bit) \| Any modern GPU \| ~1GB \|
	\| 1.5B (CPU) \| None \| ~8GB RAM \|

	---

	## Training Details

	- Method: LoRA (Low-Rank Adaptation)
	- LoRA Rank: 8
	- LoRA Alpha: 16
	- Target Modules: All linear layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
	- Epochs: ~0.8
	- Final Loss: 0.0205
	- Data Source: Stack Overflow Q&A (Python-heavy)

	---

	## Quick Links

	- [GitHub Repository](https://github.com/my-ai-stack/stack-2.9)
	- [HuggingFace Space (Demo)](https://huggingface.co/spaces/my-ai-stack/stack-2-9-demo)
	- [Base Model](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B)

	---

	## Limitations

	- Model Size: At 1.5B parameters, smaller than state-of-the-art models (7B, 32B)
	- Training Data: Primarily Python-focused; other languages may have lower quality
	- Hallucinations: May occasionally generate incorrect code; verification recommended

	---

	## Citation

	```bibtex
	@misc{my-ai-stack/stack-2-9-finetuned,
	author = {Walid Sobhi},
	title = {Stack 2.9: Fine-tuned Qwen2.5-Coder-1.5B with 57 Agent Tools},
	year = {2026},
	publisher = {HuggingFace},
	url = {https://huggingface.co/my-ai-stack/Stack-2-9-finetuned}
	}
	```

	---

	<p align="center">
	Built with ❤️ for developers<br/>
	<a href="https://discord.gg/clawd">Discord</a> · <a href="https://github.com/my-ai-stack/stack-2.9">GitHub</a> · <a href="https://huggingface.co/my-ai-stack">HuggingFace</a>
	</p>