Update README.md

README.md

@@ -5,4 +5,103 @@ datasets:
 base_model:
 - HuggingFaceTB/SmolLM2-360M-Instruct
 library_name: transformers
----
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- trl
+- text-generation-inference
+- re-think
+- r1
+---
+
+![Add a heading.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/HWLZRJqFt1tOH8IjOyDHf.png)
+
+# **SmolLM2-Rethink-360M**
+
+> **SmolLM2-Rethink-360M** is an experimental lightweight reasoning model trained on the **Celestia3-DeepSeek-R1-0528** dataset. Built on top of the **SmolLM2-135M-Instruct** architecture and scaled to 360M parameters, it is designed to enhance lightweight reasoning, logical deduction, and structured response generation—all while maintaining efficiency for resource-constrained environments.
+
+---
+
+## **Key Highlights**
+
+1. **Compact Yet Powerful**
+   With 360M parameters, the model balances performance and efficiency, offering solid reasoning capabilities with fast inference speeds.
+
+2. **Reasoning-Oriented Training**
+   Fine-tuned on instruction-tuned datasets like **Celestia3-DeepSeek-R1-0528**, optimized for logical step-by-step thinking.
+
+3. **Optimized for Edge & Research**
+   Usable on mid-range GPUs or CPU environments, making it ideal for experimentation, teaching, and lightweight deployment.
+
+4. **Structured Generation Support**
+   Capable of outputting well-organized content such as JSON, lists, workflows, and tabular formats.
+
+---
+
+## **Quickstart with 🤗 Transformers**
+
+```python
+%%capture
+!pip install transformers
+```
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "prithivMLmods/SmolLM2-Rethink-360M"
+device = "cuda"  # or "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
+
+messages = [{"role": "user", "content": "What is gravity?"}]
+input_text = tokenizer.apply_chat_template(messages, tokenize=False)
+print(input_text)
+
+inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
+outputs = model.generate(
+    inputs,
+    max_new_tokens=1024,
+    temperature=0.2,
+    top_p=0.9,
+    do_sample=True
+)
+
+print(tokenizer.decode(outputs[0]))
+```
+
+---
+
+## **Intended Use**
+
+* **Lightweight Reasoning Tasks**
+  Suitable for compact agents needing reasoning abilities without high compute requirements.
+
+* **Educational & Research Assistants**
+  Ideal for logic tutors, student aides, or research prototypes.
+
+* **Instruction Following & Structured QA**
+  Excels in scenarios requiring concise, step-by-step or well-formatted responses.
+
+* **Microservices & Embedded AI**
+  Can be embedded in systems with modest hardware, enabling distributed or modular AI.
+
+---
+
+## **Limitations**
+
+1. **Knowledge Scope**
+   Smaller models naturally have less factual coverage compared to large-scale LLMs.
+
+2. **Context Length**
+   Best used with shorter prompts and outputs due to token and memory constraints.
+
+3. **Variability in Creative Tasks**
+   Less suited for imaginative writing or nuanced creative expression.
+
+4. **Limited Real-World Awareness**
+   Model does not have real-time or post-training data awareness.
+
+5. **Prompt Sensitivity**
+   Outputs can vary based on phrasing; best results come from clear, guided prompts.