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--- |
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base_model: unsloth/gemma-2-2b-it |
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library_name: peft |
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--- |
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## Model Summary |
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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. |
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## Motivation |
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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. |
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## Usage |
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```python |
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from unsloth import FastLanguageModel |
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import torch |
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from transformers import TextStreamer |
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max_seq_length = 3072 |
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dtype = None |
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load_in_4bit = False |
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lora_path = "/altaidevorg/gemma-altai-2-2b-reasoning" |
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use_streamer = False |
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model, tokenizer = FastLanguageModel.from_pretrained( |
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model_name=lora_path, |
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max_seq_length=max_seq_length, |
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dtype=dtype, |
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load_in_4bit=load_in_4bit, |
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) |
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FastLanguageModel.for_inference(model) |
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text_streamer = TextStreamer(tokenizer, skip_prompt = True) |
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) |
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messages = [ |
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{"role": "user", "content": user_prompt}, |
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] |
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input_ids = tokenizer.apply_chat_template( |
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messages, |
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tokenize = True, |
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add_generation_prompt = True, |
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return_tensors = "pt", |
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).cuda() |
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terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<end_of_turn>")] |
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outputs = model.generate(input_ids = input_ids, |
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streamer = text_streamer if use_streamer else None, |
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max_new_tokens = 1024, |
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eos_token_id=terminators, |
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use_cache=True, do_sample=True, temperature=0.6, top_p=0.9) |
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if not use_streamer: |
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out = outputs[0][input_ids.shape[-1]:] |
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generated_text = tokenizer.decode(out, skip_special_tokens=True) |
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print(generated_text) |
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``` |
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## Dataset |
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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. |
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The dataset will be made publicly available through its dedicated repository [altaidevorg/thinking-dataset-en](https://huggingface.co/datasets/altaidevorg/thinking-dataset-en). |
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