SupraLabs released supra-title-FFT-preview, a full fine-tuned 0.4B parameter model for chat title generation, trained on 115K samples — nearly 10x larger than their previous dataset.
Hey r/LocalLLaMA! Following up on Supra-Title-350M-exp (our first chat title generation model), we're releasing supra-title-FFT-preview, trained on a much larger and cleaner dataset. 🤗 supra-title-FFT-preview What changed Our first chat title model was trained on 12K samples (chat-titles-12K) and it showed: decent on common conversation patterns, weak on niche topics. This release is trained on 115K samples from a new filtered dataset, chat-titles-filtered-115K. Model Dataset size Supra-Title-350M-exp 12K samples supra-title-FFT-preview 115K samples Same base, same task, just a lot more coverage. Per our naming convention, this is the last checkpoint before the final non-preview release. Specs Spec Value Base model LiquidAI/LFM2.5-350M-Base Parameters ~0.4B Precision BF16 Training Full fine-tune (FFT), not LoRA Framework Unsloth Task Single-purpose: chat title generation Still no system prompt needed. Send the user message, get a title back. Quick start Transformers pipeline: from transformers import pipeline pipe = pipeline("text-generation", model="SupraLabs/supra-title-FFT-preview") messages = [{"role": "user", "content": "bruh my wifi keeps disconnecting every 10 minutes"}] print(pipe(messages)) Or load directly: from transformers import AutoTokenizer, AutoModelForCausalLM import torch MODEL_ID = "SupraLabs/supra-title-FFT-preview" tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto") messages = [{"role": "user", "content": "what's the easiest way to make fluffy pancakes?"}] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)) vLLM (OpenAI-compatible server): vllm serve "SupraLabs/supra-title-FFT-preview" curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SupraLabs/supra-title-FFT-preview", "messages": [{"role": "user", "content": "What is the capital of France?"}] }' Apache 2.0. This is a preview checkpoint, feedback on edge cases and weird titles is genuinely useful before we lock the final version.
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