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RemoraAI
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Model Details
Model Provider
RemoraAI
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
28,771 instruction pairs generated from Remora's own tables (September–October 2026, Solana mainnet, Helius data):
| family | what the record holds | what the answer does |
|---|---|---|
| token post-mortem | graduation time, creator and their prior graduates, first-window buyers and SOL flows, +10 min and +1 h marks, dead-by-1h flag, socials | narrates the first hour, gives a hindsight verdict |
| wallet dossier | tokens traded, closed positions, win count, SOL spent/received, realized PnL, median hold | classifies the style (bot, flipper, intraday, holder) and says whether it is worth copying |
| creator profile | graduates in 90 days, first/last dates, base rates for factory vs one-off creators | flags launch factories |
| KOL call behaviour | followers, matched tokens, share bought before posting, median lead, share sold within 1 h | says whether followers are the exit liquidity |
| strategy explanation | first-window features, model score, rule-exit outcome | explains why a token was ranked and how it went |
| token naming | pump.fun name/ticker/description | writes or names in the pump.fun register |
| no record | question only | declines to invent figures and asks for the record |
Base model Qwen3-8B, QLoRA (r=32, α=64, all attention and MLP projections), 2 epochs, 19.4M tokens, final train loss 0.97. Trained with TRL on one H100 in 76 minutes.
python
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport json, torch base = "Qwen/Qwen3-8B"tok = AutoTokenizer.from_pretrained(base)model = PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto"), "RemoraAI/remora-lite-8b-v1") record = {"mint": "…", "name": "Example", "symbol": "EXM", "graduated_at": "2026-10-01 12:00 UTC", "creator": "…", "creator_prior_graduates_90d": 5, "first_window_seconds": 94, "first_window_buyers": 61, "first_window_sol_in": 212.4, "first_window_sol_out": 48.1, "return_window_end_to_10m": -0.31, "return_10m_to_1h": -0.72, "dead_by_1h": False, "wallets_trading_at_1h": 14}msgs = [{"role": "system", "content": "You are Remora, an on-chain analyst for Solana memecoins. A Remora record (JSON) may be attached to the question: answer only from it, cite its numbers, and interpret them. If no record is attached, say you need the record; never invent figures."}, {"role": "user", "content": "Was $EXM a good buy at +10 minutes?\n\nRecord:\n" + json.dumps(record)}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", enable_thinking=False).to(model.device)print(tok.decode(model.generate(ids, max_new_tokens=300, do_sample=False)[0][ids.shape[1]:], skip_special_tokens=True))Records in this exact shape are produced by the open Remora pipeline (remora CLI, see the GitHub repo) from Helius data; the SFT dataset shows every field.
Held-out records (never seen in training): share of numbers in the model's answer that appear in the reference answer: 100% on the first 12 held-out records (a larger run is pending; v0, trained without records in the prompt, scored 37% and hallucinated figures; this version reads them from the record).
Apache-2.0 (adapter and dataset). Base model Qwen3-8B is Apache-2.0.
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport json, torch base = "Qwen/Qwen3-8B"tok = AutoTokenizer.from_pretrained(base)model = PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="auto"), "RemoraAI/remora-lite-8b-v1") record = {"mint": "…", "name": "Example", "symbol": "EXM", "graduated_at": "2026-10-01 12:00 UTC", "creator": "…", "creator_prior_graduates_90d": 5, "first_window_seconds": 94, "first_window_buyers": 61, "first_window_sol_in": 212.4, "first_window_sol_out": 48.1, "return_window_end_to_10m": -0.31, "return_10m_to_1h": -0.72, "dead_by_1h": False, "wallets_trading_at_1h": 14}msgs = [{"role": "system", "content": "You are Remora, an on-chain analyst for Solana memecoins. A Remora record (JSON) may be attached to the question: answer only from it, cite its numbers, and interpret them. If no record is attached, say you need the record; never invent figures."}, {"role": "user", "content": "Was $EXM a good buy at +10 minutes?\n\nRecord:\n" + json.dumps(record)}]ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", enable_thinking=False).to(model.device)print(tok.decode(model.generate(ids, max_new_tokens=300, do_sample=False)[0][ids.shape[1]:], skip_special_tokens=True))