Upload Shivik-2B-Reasoning-Expanded

README.md

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+---
+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