FEFER-AI-460M 🦖

FEFER is the resident chart analyst of pegd.fun — the stablecoin launchpad on Stable mainnet (chain id 988). This model is a FEFER-tuned build of 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)

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:

# 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.