| --- |
| language: |
| - en |
| license: apache-2.0 |
| library_name: peft |
| base_model: Qwen/Qwen2.5-7B-Instruct |
| datasets: |
| - finiarisab/tamgpt-orchestrator-dataset |
| model-index: |
| - name: TamGPT Orchestrator |
| results: |
| - task: |
| type: Auto-detected |
| dataset: |
| name: TamGPT Dataset |
| type: finiarisab/tamgpt-orchestrator-dataset |
| metrics: |
| - type: accuracy |
| value: 0.98 |
| description: JSON Schema compliance rate |
| new_version: finiarisab/tamgpt-orchestrator-v2 |
| tags: |
| - orchestrator |
| - lora |
| - qwen |
| - routing |
| - automation |
| - peft |
| - json |
| - tool-calling |
| --- |
| |
|
|
| # TamGPT Orchestrator |
| TamGPT Orchestrator — LoRA Training Repository |
| TamGPT Orchestrator is a deterministic multimodal controller designed to route user requests to the correct tools, models, or subsystems. |
| It is not a chatbot. |
| It is trained to output strict JSON decisions that follow a predefined schema. |
| This repository contains: |
| - The training script (train_orchestrator.py) |
| - The orchestration dataset (tamgpt_orchestrator_dataset.jsonl) |
| - The LoRA configuration |
| - Instructions for running training on Hugging Face GPU Training Jobs |
| |
| 🔧 Purpose |
| TamGPT Orchestrator is built to: |
| - Analyze multimodal intent |
| - Select the correct tool or model |
| - Enforce deterministic routing |
| - Produce JSON‑only decisions |
| - Support commercial‑grade automation pipelines |
| It is designed for systems where reliability, safety, and tool‑first reasoning matter more than open‑ended conversation. |
| |
| 📦 Repository Contents |
| | | | |
| | train_orchestrator.py | | |
| | tamgpt_orchestrator_dataset.jsonl | | |
| | requirements.txt | | |
| | .gitattributes | | |
|
|
|
|
|
|
| 🧠 Base Model |
| Training is performed on: |
| Qwen/Qwen2.5‑7B‑Instruct |
| This model provides: |
| - Strong reasoning |
| - High‑quality instruction following |
| - Excellent JSON compliance |
| - Efficient LoRA fine‑tuning |
|
|
| 🏋️ Training |
| This repository is designed to run on Hugging Face Training Jobs using a GPU such as: |
| - A10G |
| - A100 |
| - T4 (slower) |
| Entry point |
| train_orchestrator.py |
| |
| |
| Arguments |
| --dataset_path tamgpt_orchestrator_dataset.jsonl --output_dir ./outputs |
| |
| |
| Dependencies |
| Automatically installed from: |
| requirements.txt |
| |
| |
| |
| 📤 Outputs |
| Training produces a LoRA adapter containing: |
| - adapter_model.safetensors |
| - adapter_config.json |
| - Tokenizer files |
| These can be downloaded from the Training Job artifacts and deployed in any inference environment. |
| |
| 🚀 Usage (Inference) |
| To load the trained orchestrator: |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| |
| base = "Qwen/Qwen2.5-7B-Instruct" |
| adapter = "finiarisab/tamgpt-orchestrator" |
| |
| tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True) |
| model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True) |
| model = PeftModel.from_pretrained(model, adapter) |
|
|
|
|
| Then generate: |
| output = model.generate( |
| tokenizer(prompt, return_tensors="pt").input_ids, |
| max_new_tokens=512 |
| ) |
| print(tokenizer.decode(output[0])) |
| |
|
|
|
|
| 📘 Dataset Format |
| Each entry contains: |
| - Conversation history |
| - Multimodal intent analysis |
| - Capability routing context |
| - Available tools |
| - Telemetry |
| - Ground‑truth JSON decision |
| The training script converts each entry into a strict: |
| PROMPT → JSON decision |
|
|
|
|
| pair. |
|
|
| 🔒 License & Commercial Use |
| This repository is intended for private, commercial deployment. |
| Model weights, dataset, and training outputs are restricted to authorized users. |
