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interpolators/Fable-Qwen3-4B-SFT-bf16 is a compact Qwen3 4B instruction model fine-tuned on Fable 5 style agentic SFT traces and exported as merged bf16 weights.unsloth/Qwen3-4Blordx64/agentic-distill-fable-5-sfttext examples from lordx64/agentic-distill-fable-5-sft, a Fable 5 distillation-style SFT dataset. No additional private data was added.tokenizer.apply_chat_template where possible.1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4model_id = "interpolators/Fable-Qwen3-4B-SFT-bf16"
5tok = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
7
8messages = [
9 {"role": "system", "content": "You are a helpful, careful assistant."},
10 {"role": "user", "content": "Write a concise plan for debugging a failing training run."},
11]
12inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
13out = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
14print(tok.decode(out[0], skip_special_tokens=True))