This repository contains a full-parameter fine-tuned version of LiquidAI/LFM2-1.2B on the HelpingAI/Intermediate-Thinking-130k dataset. The goal of this fine-tune is to enhance the model's ability to perform step-by-step reasoning and solve problems that require intermediate thought processes.

This model was trained with high efficiency using the Unsloth library.

GGUF Quantizations: For CPU inference, GGUF versions are available in the GGUF repository here.

Model Details

Base Model: LiquidAI/LFM2-1.2B
Dataset: HelpingAI/Intermediate-Thinking-130k
Fine-tuning Method: Full Parameter Fine-tuning.
Frameworks: Unsloth, Transformers, PyTorch, TRL.

How to Use with Unsloth

You can easily run this model for fast inference using the unsloth library.

First, install the necessary packages:

bash

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps git+https://github.com/huggingface/transformers.git
pip install --no-deps causal-conv1d==1.5.0.post8

Next, use the following Python code to load the model and generate text:

python

from unsloth import FastModel
import torch
from transformers import TextStreamer

model_repo_id = "kreasof-ai/Liquid-Thinking-Preview" 

model, tokenizer = FastModel.from_pretrained(
    model_name = model_repo_id,
    dtype = None,      # None for auto detection
    load_in_4bit = True, # Use 4bit quantization for faster inference
)

# Define the conversation
messages = [{
    "role": "user",
    "content": "Explain the step-by-step process of photosynthesis in simple terms.",
}]

# Apply the chat template
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt = True, # Must add for generation
    return_tensors = "pt",
).to("cuda")

# Set up the streamer for real-time output
streamer = TextStreamer(tokenizer, skip_prompt = True)

# Generate the response
_ = model.generate(
    **inputs,
    max_new_tokens = 4096,
    # Recommended generation parameters from LiquidAI
    temperature = 0.3, 
    min_p = 0.15, 
    repetition_penalty = 1.05,
    use_cache = True,
    streamer = streamer,
)

Training Procedure

Data Preprocessing

The HelpingAI/Intermediate-Thinking-130k dataset, consisting of instruction and output pairs, was formatted into a conversational format using the LFM2's chat template. The template follows this structure:

<|im_start|>user
{instruction}<|im_end|>
<|im_start|>assistant
{output}<|im_end|>

The training loss was calculated only on the assistant's responses (train_on_responses_only), encouraging the model to learn the desired output format and reasoning style without being penalized for the user's prompt.

Hyperparameters

The model was trained with the following hyperparameters using trl.SFTTrainer:

Hyperparameter	Value
`per_device_train_batch_size`	8
`gradient_accumulation_steps`	4
`Effective Batch Size`	32
`num_train_epochs`	3
`learning_rate`	4e-5
`lr_scheduler_type`	`cosine`
`warmup_ratio`	0.1
`optim`	`adamw_torch`
`weight_decay`	0.01
`max_seq_length`	8192
`seed`	3407

Training Results

The training loss curve showed a steady decrease, indicating successful learning.

Intended Use & Limitations

This model is intended for research and experimentation in multi-step reasoning, complex problem-solving, and chain-of-thought generation.

Limitations: Like all language models, it is prone to hallucination and may produce factually incorrect or biased information.
Out-of-Scope: This model is not designed for production use without extensive testing and safety evaluations. It should not be used for critical applications where incorrect information could cause harm.