updated

README.md

@@ -4,19 +4,67 @@ language:
 - en
 license: apache-2.0
 tags:
-- text-generation-inference
+- llm-finetuning
 - transformers
 - unsloth
 - mistral
 - trl
+datasets:
+- stanfordnlp/imdb
 ---
 
-# Uploaded  model
+# Uploaded Model
 
-- **Developed by:** devshaheen
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
+- **Developed by:** Shaheen Nabi
+- **License:** Apache-2.0
+- **Finetuned from model:** `unsloth/mistral-7b-bnb-4bit`
+- **Model Type:** Large Language Model (LLM)
+- **Training Framework:** Hugging Face Transformers, TRL (Transformers Reinforcement Learning) library
+- **Pretraining Dataset:** [Stanford IMDb Dataset](https://huggingface.co/datasets/stanfordnlp/imdb)
+- **Fine-Tuning Dataset:** Stanford IMDb (Text Classification Task)
 
-This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+### Overview
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+This model is a fine-tuned version of `unsloth/mistral-7b-bnb-4bit`, a 7-billion-parameter model based on the Mistral architecture. It was trained to improve performance on natural language understanding tasks, specifically for text classification using the Stanford IMDb dataset. 
+
+The fine-tuning process leveraged the **Unsloth** framework, which speeds up training times significantly, enabling a **2x faster training** process compared to traditional methods. Additionally, Hugging Face's **TRL library** (Transformers Reinforcement Learning) was used to adapt the model efficiently.
+
+### Training Details
+
+- **Base Model:** `unsloth/mistral-7b-bnb-4bit` (7B parameters, 4-bit quantized weights for memory efficiency)
+- **Training Speed:** The model was trained **2x faster** with Unsloth, making it a more practical solution for large-scale fine-tuning.
+- **Optimization Techniques:** Use of low-rank adaptation (LoRA), gradient checkpointing, and 4-bit quantization to reduce memory and computational cost while maintaining model performance.
+
+### Intended Use
+
+This model is intended for tasks like:
+- Sentiment analysis
+- Text classification
+- Fine-grained NLP tasks
+
+It is well-suited for environments with limited resources, thanks to the quantization of the base model and fine-tuning techniques employed.
+
+### Model Performance
+
+- **Primary Metric:** Accuracy on text classification tasks (Stanford IMDb dataset)
+- **Fine-Tuning Results:** This fine-tuned model achieved a notable improvement in accuracy, making it suitable for deployment in real-world NLP applications.
+
+### Usage
+
+To use the model, you can directly load it using Hugging Face's Transformers library, with the following code:
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+model_name = "shaheennabi/your-finetuned-mistral-7b-imdb"
+
+# Load the fine-tuned model
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Example of using the model for inference
+input_text = "This movie was fantastic!"
+inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
+outputs = model(**inputs)