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license: apache-2.0
base_model: qvac/VisionPsy-Nano-460M
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- vision-language-model
- nanovlm
- chart-understanding
- ocr
- crypto
- launchpad
- stable-mainnet
- fefer
- pegd-fun
language:
- en
---
<p align="center">
<img src="fefer-512.png" width="180" alt="FEFER AI" />
</p>
# FEFER-AI-460M 🦖
**FEFER** is the resident chart analyst of [pegd.fun](https://pegd.fun) — the stablecoin
launchpad on **Stable mainnet (chain id 988)**. This model is a FEFER-tuned build of
[`qvac/VisionPsy-Nano-460M`](https://huggingface.co/qvac/VisionPsy-Nano-460M)
(Tether AI Research), a ~460M-parameter vision-language model (SigLIP2 vision encoder +
SmolLM2-360M LM, nanoVLM architecture) that punches far above its size on chart
understanding, OCR, and visual instruction following.
FEFER reads **one thing and one thing only**: terminal-style candlestick charts rendered by
the pegd.fun indexer for Stable-988 launchpad tokens. Each chart has verified indexer facts
(market cap, 24h volume, graduation progress) printed directly on the image, so the model
cross-checks pixels against ground truth instead of inventing numbers.
## What it's for
- Reading pegd.fun 1024×512 candle charts: trend, momentum, volume behavior, drawdowns
- Answering holder questions in the FEFER persona (plain language, risk note always included)
- Explaining launchpad mechanics it was trained on: fixed 1B supply, permanently locked
Uniswap V3 1% USDT0 pools, 3,000 USDT0 opening FDV, 9,000 USDT0 graduation, 80/20
creator/platform fee split
## What it's NOT for
- Price prediction or financial advice — FEFER always says so, by training and by prompt
- Multi-image reasoning (single-image by architecture)
- Anything outside Stable mainnet 988 — other chains are out of scope on purpose
## Quickstart (transformers)
```python
from transformers import AutoModelForImageTextToText, AutoProcessor
from PIL import Image
repo = "feferai/FEFER-AI-460M" # replace after publishing
model = AutoModelForImageTextToText.from_pretrained(repo, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(repo, trust_remote_code=True)
chart = Image.open("fefer_chart.png") # a pegd.fun /api/fefer/chart/<token>.png render
messages = [{"role": "user", "content": [
{"type": "image", "image": chart},
{"type": "text", "text": "Read this chart. Trend, volume, and graduation status?"},
]}]
inputs = processor.apply_chat_template(messages, add_generation_prompt=True,
tokenize=True, return_dict=True, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=320)
print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])
```
## Serving for pegd.fun
Any OpenAI-compatible endpoint works. Then point the launchpad at it:
```bash
# GPU
vllm serve feferai/FEFER-AI-460M --trust-remote-code --port 8000
# or CPU (no GPU needed at 460M)
python fefer-ai/serve_cpu.py --model feferai/FEFER-AI-460M --port 8008
# pegd.fun server env
FEFER_AI_URL=http://127.0.0.1:8000/v1
FEFER_AI_MODEL=feferai/FEFER-AI-460M
```
## Training
LoRA fine-tune on the language-model attention projections (vision tower frozen), on
chart/answer pairs harvested from live pegd.fun launches by `fefer-ai/build_dataset.mjs`
— every sample is a real indexer-rendered chart paired with facts computed from on-chain
trade history, so supervision is grounded, not synthetic guesswork. Merged with
`merge_and_unload` for standalone serving.
## Limitations & bias
460M parameters is small: FEFER is a sharp chart *reader*, not an oracle. It can misread
dense wicks, and it inherits any biases of the base model. Launchpad tokens are volatile;
nothing this model outputs is financial advice, and it is trained to say exactly that.
## License & attribution
Apache 2.0, same as the base model. Built on **VisionPsy-Nano-460M by Tether AI Research
(qvac)** — all credit for the base architecture and pretraining to them. FEFER branding,
chart pipeline, dataset tooling, and fine-tune by the pegd.fun team.
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