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interpolators
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interpolators
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unsloth/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
unsloth/Qwen3-4Blordx64/agentic-distill-fable-5-sftThe training data uses ChatML-style text examples from lordx64/agentic-distill-fable-5-sft, a Fable 5 distillation-style SFT dataset. No additional private data was added.
This model is intended for research and experimentation with Fable-style agentic behavior, compact assistant models, and Qwen3-family downstream adaptation.
Use the Qwen chat template through tokenizer.apply_chat_template where possible.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "interpolators/Fable-Qwen3-4B-SFT-bf16"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") messages = [ {"role": "system", "content": "You are a helpful, careful assistant."}, {"role": "user", "content": "Write a concise plan for debugging a failing training run."},]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)print(tok.decode(out[0], skip_special_tokens=True))This is a quick research fine-tune and has not yet gone through a full benchmark suite. Evaluate carefully before relying on it for production use.
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "interpolators/Fable-Qwen3-4B-SFT-bf16"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") messages = [ {"role": "system", "content": "You are a helpful, careful assistant."}, {"role": "user", "content": "Write a concise plan for debugging a failing training run."},]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)print(tok.decode(out[0], skip_special_tokens=True))