---
base_model: unsloth/gemma-2-2b-it
library_name: peft
---

## Model Summary

This model is fine-tuned from gemma-2-2b-it using a thinking dataset meticulously crafted by our team, aiming to enhance the model's ability to solve complex, sequential problems through step-by-step logical thinking.

## Motivation

Reasoning is a cornerstone of effective problem-solving, yet many of the models trained for this task are quite large and too heavy for everyday usage, particularly when their extra long responses considered. To address this, we developed a reasoning-focused compact language model (LLM) capable of structured thinking, self-reflection, and iterative problem-solving. Our goal is to create a model that not only excels in reasoning tasks but also operates efficiently for broader accessibility.

## Usage

```python

from unsloth import FastLanguageModel
import torch
from transformers import TextStreamer


max_seq_length = 3072
dtype = None
load_in_4bit = False
lora_path = "/altaidevorg/gemma-altai-2-2b-reasoning"
use_streamer = False

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=lora_path,
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
)
FastLanguageModel.for_inference(model) 

text_streamer = TextStreamer(tokenizer, skip_prompt = True)
)

messages = [
    {"role": "user", "content": user_prompt},
]


input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True,
    return_tensors = "pt",
).cuda()

terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<end_of_turn>")]

outputs = model.generate(input_ids = input_ids,
    streamer = text_streamer if use_streamer else None,
    max_new_tokens = 1024,
    eos_token_id=terminators,
    use_cache=True, do_sample=True, temperature=0.6, top_p=0.9)
if not use_streamer:
    out = outputs[0][input_ids.shape[-1]:]
    generated_text = tokenizer.decode(out, skip_special_tokens=True)
    print(generated_text)


```


## Dataset

The dataset was prepared through a comprehensive process based on our open-source reasoning and thinking dataset collection method. We curated and refined existing open-source datasets focusing on logical reasoning, critical thinking, and problem-solving. These datasets were preprocessed and structured for fine-tuning large language models to ensure high-quality outputs.
The dataset will be made publicly available through its dedicated repository [altaidevorg/thinking-dataset-en](https://huggingface.co/datasets/altaidevorg/thinking-dataset-en).