README.md

---
license: apache-2.0
language:
- en
tags:
- reasoning
- chain-of-thought
- cot
- thinking
- llama
base_model: Qwen/Qwen2.5-1.5B
pipeline_tag: text-generation
---

# Shivik-2B-Reasoning-Expanded

A reasoning-optimized language model with Chain-of-Thought (CoT) capabilities using `<think>` tags.

## Model Details

| Property | Value |
|----------|-------|
| Parameters | Unknown |
| Hidden Size | Unknown |
| Layers | Unknown |
| Context Length | Unknown |
| CoT Support | ✅ Yes (`<think>` tags) |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "shivash/Shivik-2B-Reasoning-Expanded"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

# For reasoning tasks, the model uses <think> tags
prompt = "Solve this step by step: What is 15% of 80?"

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

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True,
)

response = tokenizer.decode(outputs[0], skip_special_tokens=False)
print(response)
```

## Chain-of-Thought Format

The model uses `<think>` tags for internal reasoning:

```
<think>
Let me work through this step by step...
15% means 15/100 = 0.15
0.15 × 80 = 12
</think>

The answer is 12.
```

## Training

This model was trained on reasoning datasets with Chain-of-Thought demonstrations.

## License

Apache 2.0