prime-lab-wordle

LoRA adapter weights from a Prime RL run on primeintellect/wordle.

• Run ID: p5cd4iqxptn7o4s2zphhb1yx
• Adapter ID: w8conyurm3xgjplatw7oax2n
• Base model: PrimeIntellect/Qwen3-0.6B-Reverse-Text-SFT

Files

• adapter_model.safetensors - LoRA adapter weights
• adapter_config.json - PEFT adapter configuration
• run_wordle_adapter.py - simple local inference script

Quickstart

pip install torch transformers peft accelerate safetensors
python run_wordle_adapter.py \
  --base-model PrimeIntellect/Qwen3-0.6B-Reverse-Text-SFT \
  --adapter-path . \
  --prompt "You are playing Wordle. Guess the next 5-letter word: _ A _ E _"

Transformers Snippet

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model_id = "PrimeIntellect/Qwen3-0.6B-Reverse-Text-SFT"
adapter_repo = "burtenshaw/prime-lab-wordle"

tok = AutoTokenizer.from_pretrained(base_model_id)
base = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
model = PeftModel.from_pretrained(base, adapter_repo).eval()

messages = [{"role": "user", "content": "Guess a Wordle word from _ A _ E _"}]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=64)

print(tok.decode(out[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

Notes

• This repo contains an adapter, not a merged full model checkpoint.
• You must load the adapter on top of the base model listed above.