YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
tocsa/atlas-sft
Atlas customer-support triage (capstone, Section 5) โ SFT model.
Atlas is a fictional SaaS project-management tool. The model reads an incoming support ticket and returns a category, an urgency, and a short drafted response โ the Meridian pattern (eval -> SDG -> SFT -> DPO) applied end-to-end to a new domain during the capstone.
Base model / architecture
nvidia/Mistral-NeMo-Minitron-8B-Instruct (MistralForCausalLM)
Pipeline position
SFT model: a LoRA adapter trained on synthetically generated data, merged into the base model. This is the checkpoint used as the DPO starting point.
Files
config.json(702.0B)generation_config.json(111.0B)model.safetensors(15.7GB)special_tokens_map.json(414.0B)tokenizer.json(8.8MB)tokenizer_config.json(173.4KB)
Recorded metrics
baseline_eval.json
- n: 12
best_sft.json
- adapter_path: /scratch/capstone_sft/checkpoints/epoch_2_step_56/model
- epoch: 2
How to load
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("tocsa/atlas-sft")
tokenizer = AutoTokenizer.from_pretrained("tocsa/atlas-sft")
Course context
Produced in the NVIDIA instructor-led workshop "Adding New Knowledge to LLMs" โ Designing and building model-customization pipelines for domain workflows.
The workshop's build-time customization workflow:
| Stage | What it does | Tool |
|---|---|---|
| Task definition | taxonomy, schema, success criteria | |
| Eval | measure target behavior | vLLM |
| Gap evidence | baseline + ICL ceiling | |
| Synthetic data | targeted examples | NeMo Data Designer |
| Curation | quality + dedup | NeMo Curator |
| SFT | LoRA task behavior | NeMo AutoModel + PEFT |
| Serve + eval | compare model stages | vLLM |
| DPO | preference behavior | NeMo RL |
| Model artifact | adapter or DPO model |
Open-model ecosystem used across the workshop:
| Models | Data | Training | Serving |
|---|---|---|---|
| Nemotron | NeMo Data Designer | NeMo AutoModel | vLLM |
| Open weights | NeMo Curator | LoRA / PEFT | NGC containers |
| Teacher models | Hugging Face | NeMo RL | Local eval |
Training data for this run was synthetically generated (NeMo Data Designer) and/or curated during the workshop exercises. Educational artifact โ not reviewed for production use.
- Downloads last month
- 23