Update README.md

README.md

@@ -13,5 +13,64 @@ tags:
 - Llama
 - trl
 ---
+![d9-mAgyravvwWXZGi3sK5.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/jTUNV5nFY_tyhYQM-zeXl.png)
 
-![d9-mAgyravvwWXZGi3sK5.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/jTUNV5nFY_tyhYQM-zeXl.png)
+# **SmolLM2-360M-Grpo-r999**
+
+SmolLM2-360M-Grpo-r999 is fine-tuned based on **SmolLM2-360M-Instruct**. SmolLM2 demonstrates significant advances over its predecessor, SmolLM1, particularly in instruction following, knowledge, and reasoning. The **360M** model was trained on **2 trillion tokens** using a diverse combination of datasets: **FineWeb-Edu, DCLM, The Stack**, along with new filtered datasets that we curated and will release soon. We developed the instruct version through **supervised fine-tuning (SFT)** using a combination of public datasets and our own curated datasets.
+
+### **How to Use**
+
+### Transformers
+```bash
+pip install transformers
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+checkpoint = "prithivMLmods/SmolLM2-360M-Grpo-r999"
+
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
+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=50, temperature=0.2, top_p=0.9, do_sample=True)
+print(tokenizer.decode(outputs[0]))
+```
+
+### **Limitations of SmolLM2-360M-Grpo-r999**
+
+1. **Model Size**: While **360M parameters** provide enhanced capabilities, the model still has limitations in handling highly complex reasoning tasks or long-context dependencies compared to larger models.
+
+2. **Bias and Inaccuracy**: Despite fine-tuning on diverse datasets, the model may generate biased, inaccurate, or factually incorrect responses, particularly for niche topics or specialized knowledge areas.
+
+3. **Context Length**: The model might struggle with very long conversations or extended prompts, potentially leading to truncation or loss of contextual coherence.
+
+4. **Fine-Tuning Specificity**: Performance on specialized domains may require additional fine-tuning with domain-specific datasets.
+
+5. **Generalization**: The model may not generalize as effectively to **rare queries** or **unseen tasks** compared to larger models, sometimes providing generic or incomplete answers.
+
+6. **Limited Multi-Turn Conversations**: While it supports multi-turn interactions, its ability to retain and use context over extended conversations is **not as strong as larger models**.
+
+### **Intended Use of SmolLM2-360M-Grpo-r999**
+
+1. **General-purpose Conversational AI** – Ideal for chatbots, virtual assistants, and interactive applications requiring basic reasoning and knowledge retrieval.
+
+2. **Education & Tutoring** – Supports answering educational queries, explaining concepts, and aiding learning across multiple domains.
+
+3. **Content Generation** – Can generate short-form text, summaries, and brainstorming ideas for writing assistants or creativity tools.
+
+4. **Code Assistance** – Fine-tuned on programming datasets, making it useful for debugging, explaining code, and assisting developers.
+
+5. **Instruction Following** – Optimized for following structured commands, making it suitable for task-based applications.
+
+6. **Prototyping & Experimentation** – Lightweight model for **fast deployment** in new AI applications, balancing performance with efficiency.
+
+7. **Low-Resource Environments** – Runs on **edge devices, mobile apps, and local servers** where larger models are infeasible.
+
+8. **Research & Development** – Can be used as a base model for **further fine-tuning** or model optimizations.