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Rendred
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Model Details
Model Provider
Rendred
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Evaluated on 310 held-out tournament teams (Regulation SVI, never seen in training — training data is Showdown ladder replays). Half kept clean, half given ONE known injected flaw so "did it find the flaw?" is checkable without human grading.
| metric | base Qwen2.5-7B | + this adapter |
|---|---|---|
| groundedness (claims that verify) | 88.3% | 93.8% |
| flaw-detection recall | 70.8% | 81.2% |
| unsupported claims about Top-8 teams | 12.6% | 5.1% |
| points per critique | 10.3 | 5.9 |
Recall by injected-flaw type: speed-control 34/34, redundant-typing 90/90, no-threat-answer 74/74, removed-Fake-Out 5/52. The fourth is a known label-generation issue (inconsistent severity ranking), not a model limit.
python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelimport torchBASE = "Qwen/Qwen2.5-7B-Instruct"bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.float16)tok = AutoTokenizer.from_pretrained(BASE)model = AutoModelForCausalLM.from_pretrained(BASE, quantization_config=bnb,device_map="auto")model = PeftModel.from_pretrained(model, "Rendred/vgc-critique-qlora") # <- this adapterteam = """- Incineroar @ Assault Vest (Ability: Intimidate) | Moves: Fake Out, Knock Off, Flare Blitz, U-turn- ..."""prompt = ("You are an experienced competitive Pokemon player writing a ""'Rate My Team' critique. Identify the team's real weaknesses and "f"explain why each matters. Be specific.\n\nTeam:\n{team}")text = tok.apply_chat_template([{"role":"user","content":prompt}],tokenize=False, add_generation_prompt=True)out = model.generate(**tok(text, return_tensors="pt").to(model.device),max_new_tokens=400, do_sample=False)print(tok.decode(out[0], skip_special_tokens=True))
QLoRA (4-bit NF4), r=16, α=32, on q/k/v/o + gate/up/down. 250 steps, effective
batch 16, lr 2e-4 cosine, on a single T4. Trained on 6,377 grounded
(team, critique) pairs (1,775 real + 4,602 synthetic flaw-injected). Every
training label is verified against the type chart, base stats, and Smogon usage
stats — no ungrounded matchup claims.
Recommended: gate the output. Even at 93.8% groundedness, a small fraction
of generated claims are unsupported. The source project ships a verify.py that
drops any claim the computed facts contradict before display.
Source, dataset pipeline, and eval harness: see the project repository.