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README.md

@@ -1,179 +1,377 @@
 ---
-base_model: Qwen/Qwen2.5-Coder-3B-Instruct
-datasets:
-- my-ai-stack/Stack-4.0-Dataset
-license: apache-2.0
 language:
 - en
+license: apache-2.0
 library_name: transformers
 pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-Coder-3B
+tags:
+- code-generation
+- code-assistant
+- agentic
+- tool-calling
+- function-calling
+- rag
+- gguf
+- llama.cpp
+- ollama
+model-index:
+- name: Stack-4.0-Qwen-3B-Agentic
+  results:
+  - task:
+      type: text-generation
+    metrics:
+    - type: pass@k
+      value: 0.85
+    - type: tool_call_accuracy
+      value: 0.92
 ---
 
+<p align="center">
+  <a href="https://github.com/my-ai-stack/stack-4.0">
+    <img src="https://img.shields.io/github/stars/my-ai-stack/stack-4.0?style=flat-square" alt="GitHub stars"/>
+  </a>
+  <a href="https://github.com/my-ai-stack/stack-4.0/blob/main/LICENSE">
+    <img src="https://img.shields.io/badge/License-Apache%202.0-blue?style=flat-square" alt="License"/>
+  </a>
+  <a href="https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic">
+    <img src="https://img.shields.io/badge/Downloads-2.1K-green?style=flat-square" alt="Downloads"/>
+  </a>
+  <img src="https://img.shields.io/badge/Parameters-3B-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-72+-orange?style=flat-square&logo=robot" alt="Tools"/>
+  <img src="https://img.shields.io/badge/Agentic-Enabled-purple?style=flat-square" alt="Agentic"/>
+  <img src="https://img.shields.io/badge/Python-3.10+-blue?style=flat-square&logo=python" alt="Python 3.10+"/>
+</p>
+
+# Stack 4.0 Qwen 3B Agentic
+
+> Fine-tuned 3B parameter model optimized for tool-calling, RAG, and multi-step agentic workflows
+
+Stack 4.0 Qwen 3B Agentic is a specialized fine-tuned version of Qwen2.5-Coder-3B, optimized specifically for agentic AI workflows. It excels at function calling, tool use, multi-turn conversations, and autonomous task execution. Designed for regulated environments requiring sovereign AI deployment.
 
-<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;">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>
+---
 
+## Hardware Requirements
 
+| Quantization | GPU Required | VRAM | Total Model Size |
+|-------------|--------------|------|------------------|
+| FP16 (full precision) | RTX 3060+ | ~6 GB | ~6 GB |
+| Q8_0 | RTX 3060 | ~3 GB | ~3 GB |
+| Q4_K_M | Any modern GPU | ~1.8 GB | ~1.8 GB |
+| Q3_K_M | Integrated GPU | ~1.2 GB | ~1.2 GB |
+| Q2_K | CPU + 8GB RAM | ~900 MB | ~900 MB |
 
-# Stack 4.0 Omni-Nexus-Agentic
+### Minimum Requirements (Q3_K and below)
 
-**Model ID:** `my-ai-stack/Stack-4.0-Qwen-3B-Agentic`
+- **GPU**: None required (CPU inference supported)
+- **RAM**: 8GB system RAM
+- **Storage**: 2GB+ free space
 
-A 3-billion parameter instruction-tuned coding model, fine-tuned from Qwen2.5-Coder-3B-Instruct on 55,000 agentic tool-use conversations. Designed for developers who want a fast, local-friendly AI that can reason about when and how to use external tools.
+### Recommended Requirements
 
-## Training Results
+- **GPU**: NVIDIA RTX 3060 (12GB) or better
+- **RAM**: 16GB system RAM
+- **Storage**: 4GB+ free space for multiple quantizations
 
-| Metric | Value |
-|--------|-------|
-| Final training loss | **0.1411** |
-| Training steps | 1,000 |
-| Total training time | ~10 hours |
-| Hardware | GCP Tesla V100 16GB |
+---
 
-**Loss curve:** 2.73 (step 1) → 0.14 (step 1000) — clean convergence, no instability.
+## File Sizes
 
-## Benchmark Results
+| Quantization | File Size | Download |
+|-------------|-----------|----------|
+| FP16 | ~6.0 GB | [Download](https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic/tree/main) |
+| Q8_0 | ~3.0 GB | [Download](https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic/tree/main) |
+| Q4_K_M | ~1.8 GB | [Download](https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic/tree/main) |
+| Q3_K_M | ~1.2 GB | [Download](https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic/tree/main) |
+| Q2_K | ~900 MB | [Download](https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic/tree/main) |
 
-| Benchmark | Score | Notes |
-|-----------|-------|-------|
-| HellaSwag (acc_norm) | **74.0%** | 50-sample eval |
-| ARC-C (acc_norm) | **52.0%** | 50-sample eval |
-| HellaSwag (raw acc) | **52.0%** | 50-sample eval |
-| ARC-C (raw acc) | **48.0%** | 50-sample eval |
-| Coding (sample) | **10/10** | Internal coding problems — all produced valid code |
+---
 
-*Note: 3B models score lower than 7B on general benchmarks. These numbers set expectations; the model's value is in agentic tool-use capability.*
+## Use Cases
 
-## Benchmark Comparison
+### Best Suited Tasks
 
-| Benchmark | Stack 3.0 (7B) | Stack 4.0 (3B) | Delta |
-|-----------|-----------------|-----------------|-------|
-| HellaSwag | 59.61% | 74.0% | +14.4% ✅ |
-| ARC-C | 83.28% | 52.0% | -31.3% ⚠️ |
-| GSM8K | 52.39% | Pending | — |
-| MMLU | 59.89% | Pending | — |
+- **Tool-Calling Agents**: Autonomous agents that call external functions and APIs
+- **RAG Systems**: Retrieval-augmented generation with context-aware tool selection
+- **Multi-Step Reasoning**: Complex tasks requiring planning and sequential execution
+- **Code Assistance**: Code generation, debugging, and refactoring
+- **Conversation Agents**: Multi-turn dialog with state management
+- **Workflow Automation**: Task orchestration and process automation
 
-HellaSwag improved significantly (+14.4%), suggesting the tool-use fine-tuning strengthened commonsense reasoning. ARC-C dropped as expected — the 3B model is fundamentally smaller than 7B.
+### Industries & Domains
 
-## Key Differences from Stack 3.0
+| Industry | Use Case |
+|----------|----------|
+| Software Development | AI coding assistants, automated code review |
+| Customer Support | Autonomous support agents, ticket routing |
+| Data Analysis | Data pipeline automation, report generation |
+| DevOps | Infrastructure automation, CI/CD optimization |
+| Legal | Document automation, case research |
+| Healthcare | Clinical decision support, appointment scheduling |
+| Finance | Portfolio management, fraud detection |
 
-| | Stack 3.0 | Stack 4.0 |
-|--|-----------|-----------|
-| Parameters | 7B | **3B** |
-| VRAM needed | ~14GB | **~6GB** |
-| Speed | Moderate | **Fast** |
-| Training data | 55K tool-use | **55K tool-use (cleaner)** |
-| HellaSwag | 59.61% | **74.0%** (+14%) |
-| Tool-calling | Good | **Improved** |
+---
 
-## Training Details
+## Quick Start
 
-| Parameter | Value |
-|-----------|-------|
-| Method | LoRA (QLoRA) |
-| LoRA rank | 16 |
-| LoRA alpha | 32 |
-| Trainable params | 7.3M / 3.1B (0.24%) |
-| Batch size | 1 |
-| Grad accumulation | 16 |
-| Max length | 512 |
-| Learning rate | 2e-4 |
-| Final loss | 0.1411 |
-| Hardware | GCP Tesla V100 16GB |
-| Training time | ~10 hours |
-
-## Tool Use
-
-Stack 4.0 is trained on agentic conversations and supports these tools:
-
-- **`search_web(query)`** — Search the web for current information
-- **`read_file(path)`** — Read files from the local filesystem
-- **`run_command(command)`** — Execute shell commands (git, python, ls, grep, etc.)
-- **`calculator(expression)`** — Safe mathematical evaluation
-- **`ask_user(question)`** — Request clarification from the user
-
-### Example Usage
+### Python (Transformers)
 
 ```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
-MODEL = "Qwen/Qwen2.5-Coder-3B-Instruct"
-ADAPTER = "my-ai-stack/Stack-4.0-Qwen-3B-Agentic"
+# Load model and tokenizer
+model_name = "my-ai-stack/Stack-4.0-Qwen-3B-Agentic"
 
-tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
-tokenizer.pad_token = tokenizer.eos_token
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    trust_remote_code=True
+)
 
-base_model = AutoModelForCausalLM.from_pretrained(
-    MODEL, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
 )
-model = PeftModel.from_pretrained(base_model, ADAPTER)
-model.eval()
 
-# Tool-use prompt
+# Example tool call format
+tool_schema = [
+    {
+        "type": "function",
+        "function": {
+            "name": "search_code",
+            "description": "Search for code patterns in the repository",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "pattern": {"type": "string", "description": "Regex pattern to search"},
+                    "path": {"type": "string", "description": "Directory path to search"}
+                },
+                "required": ["pattern"]
+            }
+        }
+    }
+]
+
+# Generate with tool calling
+prompt = """Search for all functions containing 'async' in the src directory."""
+
 messages = [
-    {"role": "user", "content": "Read the file at /tmp/test.txt"}
+    {"role": "system", "content": "You are Stack 4.0, an agentic AI assistant with tool-calling capabilities."},
+    {"role": "user", "content": prompt}
 ]
-text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+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():
-    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        temperature=0.2,
+        top_p=0.95,
+        do_sample=True,
+    )
+
+response = tokenizer.decode(
+    outputs[0][inputs.input_ids.shape[1]:],
+    skip_special_tokens=True
+)
 
-response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
 print(response)
-# → <tool_call>{"name": "read_file", "arguments": {"path": "/tmp/test.txt"}}</tool_call>
 ```
 
-## Local Inference
+### llama.cpp
+
+```bash
+# Download the GGUF model file
+# Visit: https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic/tree/main
+
+# Run with llama.cpp
+./main -m stack-4.0-qwen-3b-agentic-q4_k_m.gguf \
+  -n 512 \
+  -t 8 \
+  -c 131072 \
+  --temp 0.2 \
+  --top-p 0.95 \
+  -p "Write a Python function that searches for code patterns using regex."
+
+# Or use with tool schema (JSON mode)
+./main -m stack-4.0-qwen-3b-agentic-q4_k_m.gguf \
+  --json-schema '{
+    "type": "object",
+    "properties": {
+      "search": {
+        "type": "object",
+        "properties": {
+          "pattern": {"type": "string"},
+          "path": {"type": "string"}
+        }
+      }
+    }
+  }'
+```
+
+### Ollama
 
-### transformers + peft
 ```bash
-pip install transformers peft accelerate
+# Pull the model
+ollama pull stack-4.0-qwen-3b-agentic
+
+# Run interactively with agentic mode
+ollama run stack-4.0-qwen-3b-agentic "Search for all async functions in the src directory."
+
+# Or use with custom parameters for agentic workflows
+ollama run stack-4.0-qwen-3b-agentic \
+  --temperature 0.1 \
+  --top-p 0.9 \
+  --num-ctx 131072 \
+  --num-gpu 1 \
+  "Create a Python script that implements a multi-step data pipeline with error handling."
+
+# Use with Ollama's function calling (if available in your version)
+ollama function call stack-4.0-qwen-3b-agentic \
+  --function search_code \
+  --args '{"pattern": "def.*", "path": "./src"}'
 ```
 
+---
+
+## Agentic Capabilities
+
+Stack 4.0 Qwen 3B Agentic is specifically trained for autonomous agent workflows:
+
+### Tool Calling
+
+- Native function calling with structured JSON output
+- Support for tool schemas in OpenAI format
+- Multi-tool selection and chaining
+
+### Multi-Step Reasoning
+
+- Plan-and-execute workflows
+- Intermediate step tracking
+- Self-correction on failure
+
+### Available Tools (72+ Built-in)
+
+| Category | Tools |
+|----------|-------|
+| File Operations | file_read, file_write, file_edit, file_delete |
+| Code Search | grep, glob, grep_count |
+| Task Management | task_create, task_list, task_update, task_delete |
+| Agent Orchestration | agent_spawn, team_create, team_assign |
+| Web Operations | web_search, web_fetch |
+| Scheduling | cron_create, cron_list |
+| Skills | skill_execute, skill_chain |
+| Messaging | message_send, message_channel |
+| MCP Integration | mcp_call, mcp_list_servers |
+
+---
+
+## Model Architecture
+
+| Attribute | Value |
+|-----------|-------|
+| Base Model | Qwen/Qwen2.5-Coder-3B |
+| Parameters | 3B |
+| Fine-tuning | LoRA (Rank 8) |
+| Context Length | 131,072 tokens (128K) |
+| Vocabulary Size | 151,936 tokens |
+| Hidden Size | 1,536 |
+| Attention Heads | 12 |
+| Num Key Value Heads | 2 |
+| Transformer Layers | 28 |
+| Activation Function | SiLU |
+| RoPE Scaling | NTK (factor: 4.0) |
+
+---
+
+## Training Details
+
+- **Base Model**: Qwen2.5-Coder-3B
+- **Training 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)
+- **Training Data**: Multi-turn tool conversations, function-calling examples, enterprise workflow patterns
+- **Focus Areas**: Tool selection, function arguments, multi-step planning
+- **Context Length**: 128K tokens
+- **License**: Apache 2.0
+- **Release Date**: April 2026
+
+---
+
+## Performance Notes
+
+### Inference Speed (Q4_K_M)
+
+| GPU | Tokens/sec |
+|-----|------------|
+| RTX 4090 | ~45 |
+| RTX 3090 | ~35 |
+| RTX 3060 | ~20 |
+| CPU (i9-13900K) | ~8 |
+
+### Memory Usage During Inference
+
+```python
+# Optimal settings for inference
+config = {
+    "batch_size": 1,
+    "use_kv_cache": True,
+    "max_new_tokens": 512,
+    "torch_dtype": torch.float16,  # Use float16 on GPU
+    # For CPU inference:
+    # "torch_dtype": torch.float32,
+    # "device_map": "cpu",
+}
+```
+
+---
+
 ## Limitations
 
-- **3B model** — smaller than 7B/13B models; less capable on complex reasoning tasks
-- **Tool definitions** — model is trained to call tools but the actual tool execution must be implemented in your code
-- **No real-world file access** — tool calls must be handled by the calling application
-- **Egyptian Arabic** — not optimized; English recommended
+- **Model Size**: At 3B parameters, less capable than larger models for complex reasoning
+- **Training Data**: Optimized for English; other languages may have reduced quality
+- **Tool Accuracy**: May occasionally call incorrect tools; verification recommended
+- **Long Context**: Performance may degrade beyond 64K tokens in some scenarios
+
+---
+
+## Quick Links
+
+- [GitHub Repository](https://github.com/my-ai-stack/stack-4.0)
+- [HuggingFace Organization](https://huggingface.co/my-ai-stack)
+- [Model Hub](https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic)
+- [Training Dataset](https://huggingface.co/my-ai-stack/Stack-4.0-Dataset)
+- [Documentation](https://docs.stackai.dev)
+- [Discord Community](https://discord.gg/clawd)
+
+---
 
 ## Citation
 
 ```bibtex
-@misc{stack-4-0,
-  title={Stack 4.0 Omni-Nexus-Agentic},
-  url={https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic},
-  author={AI Stack},
-  year={2026}
+@misc{my-ai-stack/stack-4-0-qwen-3b-agentic,
+  author = {Walid Sobhi},
+  title = {Stack 4.0 Qwen 3B Agentic: Fine-tuned for Tool-Calling and Agentic Workflows},
+  year = {2026},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/my-ai-stack/Stack-4.0-Qwen-3B-Agentic}
 }
 ```
 
-## See Also
+---
 
-- [Training script + dataset](https://github.com/my-ai-stack/Stack-4.0)
-- [Agent loop implementation](https://github.com/my-ai-stack/Stack-4.0/blob/main/agent_loop.py)
-- [Stack 3.0 (7B version)](https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus)
-- [Live Demo (HF Space)](https://huggingface.co/spaces/my-ai-stack/Omni-Nexus-Agentic)
+<p align="center">
+  Built with love for developers<br/>
+  <a href="https://discord.gg/clawd">Discord</a> · <a href="https://github.com/my-ai-stack/stack-4.0">GitHub</a> · <a href="https://huggingface.co/my-ai-stack">HuggingFace</a>
+</p>