Text Generation
Transformers
English
qwen2
code-generation
python
fine-tuning
Qwen
tools
agent-framework
multi-agent
conversational
Eval Results (legacy)
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
- Transformers
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
- 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
File size: 8,134 Bytes
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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>
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