Create README.md

README.md

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+---
+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 language models struggle with tasks that require linear thought processes or adaptive strategies. 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). 
+