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

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