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my-ai-stack/
Stack-2-9-finetuned

Name: Stack-2-9-finetuned
Rating: 2.5 (11 reviews)

Qwen/Qwen2.5-Coder-1.5B

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Tools

Python 3.10+

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

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}
}

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