---
license: mit
library_name: transformers.js
base_model: teapotai/teapotllm
pipeline_tag: text-generation
tags:
- transformers.js
- onnx
- int8
- uint8
- lora
- profile-qa
datasets:
- justinthelaw/profile-qa-synthetic-public-v1
metrics:
- accuracy
---

# Teapot Profile-QA Browser 1024

## Description

This model is a browser-oriented ONNX export of a local LoRA continuation from
`teapotai/teapotllm`. It is tuned for public resume/profile Q&A prompts that fit
within a 1024-token browser context budget.

Hugging Face model metadata uses the official `text-generation` task category;
the browser runtime still loads this T5-style export with the Transformers.js
`text2text-generation` pipeline.

The target use case is a static portfolio or resume site that runs inference in
the browser with Transformers.js, without API routes, hosted inference, server
actions, or cloud training. The profile schema is intentionally generic for repo
reuse: `identity`, `current_role`, `experience`, `projects`, `education`,
`recommendations`, `skills`, and `interests`.

## Browser Artifacts

The repository payload contains tokenizer/config files at the root and
Transformers.js ONNX files under `onnx/`:

- `encoder_model_int8.onnx`
- `decoder_model_merged_int8.onnx`
- `encoder_model_uint8.onnx`
- `decoder_model_merged_uint8.onnx`

The export gate rejects external `.onnx.data` files so the model can be loaded
as self-contained browser assets.

## How to Use

```javascript
import { pipeline } from "@huggingface/transformers";

const generator = await pipeline(
  "text2text-generation",
  "justinthelaw/teapot-profile-qa-browser-1024",
  { dtype: "int8" },
);

const result = await generator(prompt, { max_new_tokens: 160 });
```

Use `dtype: "uint8"` as a browser fallback if the target environment has issues
with signed int8 ONNX weights.

## Training

- Base model: `teapotai/teapotllm`
- Method: local LoRA/QLoRA continuation, no full fine-tune and no cloud training
- Promoted checkpoint: `teapot-profile-qa-lora-v5/checkpoint-40`
- LoRA: rank 16, alpha 32, dropout 0.03, target modules `q` and `v`
- 8GB-safe settings: 4-bit base loading, batch size 1, gradient accumulation 8,
  gradient checkpointing, short eval batches
- Final continuation window: train loss 0.0330 at step 40
- Best validation eval loss: 0.0287

## Software

- Training: PyTorch, Transformers, PEFT, bitsandbytes, Datasets
- Export: Optimum ONNX export, ONNX Runtime dynamic quantization
- Browser runtime: Transformers.js with ONNX Runtime Web/WASM
- Browser packaging: `text2text-generation-with-past` export with
  `decoder_model_merged` and subgraph-enabled ONNX quantization

## Hardware

Training was designed for a local 8GB NVIDIA laptop GPU profile, with GPU
health checks for `nvidia-smi`, `/dev/nvidia*`, CUDA-enabled PyTorch, and
`torch.cuda.is_available()`. Export and card preparation can run on CPU after
training completes.

## Evaluation

| Run | Macro | Refusal Accuracy | Multi-Turn Accuracy |
| --- | ---: | ---: | ---: |
| Teapot baseline, test | 0.7114 | 0.4000 | 0.7917 |
| Promoted checkpoint, validation | 0.9792 | 1.0000 | 1.0000 |
| Promoted checkpoint, test | 0.9753 | 1.0000 | 1.0000 |

Promoted checkpoint test macro by task:

| Task | Macro |
| --- | ---: |
| `chronology` | 0.7500 |
| `education` | 1.0000 |
| `multi_hop` | 0.8214 |
| `multi_turn` | 1.0000 |
| `recommendations` | 1.0000 |
| `refusal` | 1.0000 |
| `single_turn` | 1.0000 |

## Intended Uses

- Browser-only profile or resume Q&A.
- Static portfolio demos where answers must stay grounded in public profile
  context.
- Forks that replace the included public facts with another person's public
  resume/profile sections.

## Limitations

This is not a general assistant. The dataset is synthetic and profile-specific,
so production use should regenerate data from the target person's public facts
and rerun local evaluation. The model should refuse private or unsupported facts
when the public profile context does not answer.

## Responsible AI Considerations

Keep factual context public, review generated examples for private-data leakage,
and preserve refusal examples for sensitive or absent facts such as home
addresses, phone numbers, salary, and classified information. Do not use this
model for background checks, hiring decisions, legal advice, medical advice, or
identity verification.

## Release Notes

- 2026-06-19: Initial local browser profile-QA export with `int8` and `uint8`
  ONNX variants.

## License

MIT. The base model card for `teapotai/teapotllm` also lists MIT.