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Bimaldo
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Model Details
Model Provider
Bimaldo
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel model_name = "Qwen/Qwen2.5-1.5B-Instruct"adapter = "beme08/qwen2.5-1.5b-riddle-lora" tokenizer = AutoTokenizer.from_pretrained(model_name)if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float32, trust_remote_code=True).to("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu") model = PeftModel.from_pretrained(model, adapter) def solve(riddle): messages = [ {"role": "system", "content": "Solve the riddle. Respond with only the answer and no explanation."}, {"role": "user", "content": riddle}, ] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) out = model.generate(**inputs, max_new_tokens=60, do_sample=False, pad_token_id=tokenizer.eos_token_id) return tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip() print(solve("What gets wetter the more it dries?"))# → "A towel"66 held-out riddles (same split hash 5b05f514e666c2dd as the diffusion study). Exact match uses canonicalized answers (lowercased, punctuation stripped, deduplicated tokens).
| Condition | EM | Correct |
|---|---|---|
| Diffusion Phase 3 (8M params) | 47.0% | 31/66 |
| Qwen2.5-1.5B zero-shot | 12.1% | 8/66 |
| Qwen2.5-1.5B five-shot | 31.8% | 21/66 |
| Qwen2.5-1.5B + LoRA (177 riddles) | 57.6% | 38/66 |
| Qwen2.5-1.5B + LoRA (1.2K mixed riddles) | 63.6% | 42/66 |
Multi-seed mean (seeds 42, 123, 456): 60.1% ± 3.8 pp (SD), range 56.1–63.6%.
q_proj, k_proj, v_proj, o_projExternal riddle answers were normalized to match the diffusion convention: lowercase, articles added for single-word common nouns.
Install the diffusion CLI with optional LoRA backend:
bash
pip install "riddle-diffusion[lora]"riddle-solve --model diffusion "what gets wetter as it dries"riddle-solve --model qwen-lora "what gets wetter as it dries"The LoRA backend downloads the 1.5B base model on first use (~3 GB).
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel model_name = "Qwen/Qwen2.5-1.5B-Instruct"adapter = "beme08/qwen2.5-1.5b-riddle-lora" tokenizer = AutoTokenizer.from_pretrained(model_name)if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float32, trust_remote_code=True).to("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu") model = PeftModel.from_pretrained(model, adapter) def solve(riddle): messages = [ {"role": "system", "content": "Solve the riddle. Respond with only the answer and no explanation."}, {"role": "user", "content": riddle}, ] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) out = model.generate(**inputs, max_new_tokens=60, do_sample=False, pad_token_id=tokenizer.eos_token_id) return tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True).strip() print(solve("What gets wetter the more it dries?"))# → "A towel"pip install "riddle-diffusion[lora]"riddle-solve --model diffusion "what gets wetter as it dries"riddle-solve --model qwen-lora "what gets wetter as it dries"