Update README.md

README.md

@@ -4,19 +4,84 @@ language:
 - en
 license: apache-2.0
 tags:
-- text-generation-inference
+- text-generation
+- instruction-following
 - transformers
 - unsloth
 - llama
 - trl
 ---
 
-# Uploaded  model
+![image](./image.webp)
+# SmolLM2-1.7B-Instruct
 
-- **Developed by:** Daemontatox
-- **License:** apache-2.0
-- **Finetuned from model :** HuggingFaceTB/SmolLM2-1.7B-Instruct
+**Developed by:** Daemontatox
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+**Model Type:** Fine-tuned Language Model (LLM)
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+**Base Model:** [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
+
+**Finetuned from model:** HuggingFaceTB/SmolLM2-1.7B-Instruct
+
+**License:** apache-2.0
+
+**Languages:** en
+
+**Tags:**
+- text-generation
+- instruction-following
+- transformers
+- unsloth
+- llama
+- trl
+
+## Model Description
+
+SmolLM2-1.7B-Instruct is a fine-tuned version of [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct), optimized for general-purpose instruction-following tasks. This model combines the efficiency of the LLaMA architecture with fine-tuning techniques to enhance performance in:
+
+- Instruction adherence and task-specific prompts.
+- Creative and coherent text generation.
+- General-purpose reasoning and conversational AI.
+
+The fine-tuning process utilized [Unsloth](https://github.com/unslothai/unsloth) and the Hugging Face TRL library, achieving a 2x faster training time compared to traditional methods. This efficiency allows for resource-conscious model updates while retaining high-quality performance.
+
+## Intended Uses
+
+SmolLM2-1.7B-Instruct is designed for:
+
+- Generating high-quality text for a variety of applications, such as content creation and storytelling.
+- Following complex instructions across different domains.
+- Supporting research and educational use cases.
+- Serving as a lightweight option for conversational agents.
+
+## Limitations
+
+While the model excels in instruction-following tasks, it has certain limitations:
+
+- May exhibit biases inherent in the training data.
+- Limited robustness for highly technical or specialized domains.
+- Performance may degrade with overly complex or ambiguous prompts.
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "daemontatox/smollm2-1.7b-instruct"  # Replace with the actual model name
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Example usage
+prompt = "Explain the importance of biodiversity in simple terms: "
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs)
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)
+```
+
+## Acknowledgements
+
+Special thanks to the Unsloth team for their tools enabling efficient fine-tuning. The model was developed with the help of open-source libraries and community resources.
+
+[![Unsloth Logo](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)](https://github.com/unslothai/unsloth)