BananaMind/BananaMind-Title-1.0

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BananaMind Title 1.0

BananaMind Title 1.0 is a compact chat title generation model full-finetuned from LiquidAI/LFM2.5-350M-Base.

Examples

Message:

why is my wifi disconnecting

Title:

Troubleshooting Wi-Fi Disconnection Issues

Message:

write a c# method to balance a budget

Title:

C# Budget Balancing Method

The model is intended to create neutral conversation titles. It is not intended to answer the user request.

Model Details

• —Model name: BananaMind Title 1.0
• —Base model: LiquidAI/LFM2.5-350M-Base
• —Model family: LFM2.5
• —Base parameter count: 350M
• —Training method: full finetune
• —Precision: BF16
• —Task: first user message to short chat title
• —Dataset: BananaMind/BananaMind-Chat-Titles-200K
• —Recommended max input length: 2048 tokens

<h2 align="center">Why BananaMind Title?</h2> Most AI Platforms use their most powerful model to generate titles, While this works, it uses alot of compute just for the title. BananaMind Title Focuses only on title generation and makes it only 350M Parameters which dramatically decreases the compute needed, this thing runs on a CPU Only Phone!

GGUF is coming soon.

Prompt Format

Use this prompt format for best results:

Generate a short chat title. Message: <first user message> Title:

The model should complete only the title.

<h3 align="center">Using BananaMind Title</h3>

import torch from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "BananaMind/BananaMind-Title-1.0"

tokenizer = AutoTokenizer.frompretrained( modelid, trustremotecode=True, )

if tokenizer.padtoken is None: tokenizer.padtoken = tokenizer.eos_token

model = AutoModelForCausalLM.frompretrained( modelid, dtype=torch.bfloat16, devicemap="auto", trustremote_code=True, )

model.config.use_cache = True model.eval()

message = "What is a AI?"

prompt = f"""Generate a short chat title. Message: {message} Title:"""

inputs = tokenizer( prompt, returntensors="pt", truncation=True, maxlength=768, ).to(model.device)

with torch.inferencemode(): output = model.generate( **inputs, maxnewtokens=12, dosample=False, repetitionpenalty=1.1, eostokenid=tokenizer.eostokenid, padtokenid=tokenizer.padtokenid, usecache=True, )

title = tokenizer.decode( output[0][inputs["inputids"].shape[1]:], skipspecial_tokens=True, ).strip().split("\n")[0].strip()

print(title)

Training Summary

BananaMind Title 1.0 was full-finetuned from LiquidAI/LFM2.5-350M-Base on BananaMind/BananaMind-Chat-Titles-200K.

Training format:

Generate a short chat title. Message: <first user message> Title: <target title>

Prompt/input tokens were masked from loss. The model was trained to predict only the title completion tokens.

Training setup:

• —base model: LiquidAI/LFM2.5-350M-Base
• —dataset: BananaMind/BananaMind-Chat-Titles-200K
• —method: full finetune
• —precision: BF16
• —max sequence length: 2048
• —learning rate: 3e-5
• —effective batch size: 128
• —stopped around epoch 1
• —observed eval loss near selected checkpoint: about 0.769

License and Attribution

This model is a derivative fine-tune of LiquidAI/LFM2.5-350M-Base by Liquid AI.

The base model is released under the LFM Open License v1.0. This fine-tuned model is distributed under the same license family and remains subject to the LFM Open License v1.0 terms.

Attribution:

• —Base model: LiquidAI/LFM2.5-350M-Base
• —Base model creator: Liquid AI, Inc.
• —Base model license: LFM Open License v1.0
• —Fine-tuned model: BananaMind/BananaMind-Title-1.0
• —Fine-tuned by: BananaMind
• —Training dataset: BananaMind/BananaMind-Chat-Titles-200K

Liquid AI retains ownership of the original base model. BananaMind provides the fine-tuning modifications for this derivative model.

Users should review the LFM Open License v1.0 before using, redistributing, or commercially deploying this model.

Citation

If you use this model, please attribute both the base model and this fine-tune:

BananaMind Title 1.0 is a full fine-tune of LiquidAI/LFM2.5-350M-Base for chat title generation, trained on BananaMind/BananaMind-Chat-Titles-200K.

Please retain Liquid AI attribution and license notices for the LFM base model.