Instructions to use BananaMind/BananaMind-Title-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BananaMind/BananaMind-Title-1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BananaMind/BananaMind-Title-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BananaMind/BananaMind-Title-1.0")
model = AutoModelForCausalLM.from_pretrained("BananaMind/BananaMind-Title-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BananaMind/BananaMind-Title-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BananaMind/BananaMind-Title-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BananaMind/BananaMind-Title-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BananaMind/BananaMind-Title-1.0

SGLang

How to use BananaMind/BananaMind-Title-1.0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BananaMind/BananaMind-Title-1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BananaMind/BananaMind-Title-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BananaMind/BananaMind-Title-1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BananaMind/BananaMind-Title-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BananaMind/BananaMind-Title-1.0 with Docker Model Runner:
```
docker model run hf.co/BananaMind/BananaMind-Title-1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Why BananaMind Title?

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.

Using BananaMind Title

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "BananaMind/BananaMind-Title-1.0"

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True,
)

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

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_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,
    return_tensors="pt",
    truncation=True,
    max_length=768,
).to(model.device)

with torch.inference_mode():
    output = model.generate(
        **inputs,
        max_new_tokens=12,
        do_sample=False,
        repetition_penalty=1.1,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id,
        use_cache=True,
    )

title = tokenizer.decode(
    output[0][inputs["input_ids"].shape[1]:],
    skip_special_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.

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Safetensors

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0.4B params

Tensor type

BF16

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