---
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.