Create README.md

README.md

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+---
+license: mit
+datasets:
+- Intel/orca_dpo_pairs
+---
+
+## SOLAR-10B-Nector-Orca-DPO-LoRA-Jawade
+
+### Overview
+This model card is instruction finetuned version of `upstage/SOLAR-10.7B-Instruct-v1.0` model. Trained on a mixture of Berkley Nector dataset and Intel DPO Orca dataset using LoRA.
+
+![model_card_image](SOLAR_ORCA.png)
+
+## How to Use This Model
+
+To use the model `bhavinjawade/SOLAR-10B-OrcaDPO-Jawade`, follow these steps:
+
+1. **Import and Load the Model and Tokenizer**
+   Begin by importing the model and tokenizer. Load them using the `from_pretrained` method.
+
+   ```python
+   from transformers import AutoModelForCausalLM, AutoTokenizer
+   model = AutoModelForCausalLM.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade")
+   tokenizer = AutoTokenizer.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade")
+   ```
+
+2. **Format the Prompt**
+Format the chat input as a list of messages, each with a role ('system' or 'user') and content.
+
+    ```python
+    message = [
+        {"role": "system", "content": "You are a helpful assistant chatbot."},
+        {"role": "user", "content": "Is the universe real? or is it a simulation? whats your opinion?"}
+    ]
+    prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
+    ```
+
+3. **Create a Pipeline**
+Set up a pipeline for text generation with the loaded model and tokenizer.
+
+    ```python
+    pipeline = transformers.pipeline(
+        "text-generation",
+        model=model,
+        tokenizer=tokenizer
+    )
+    ```
+
+4. **Generate Text**
+Use the pipeline to generate a sequence of text based on the prompt. You can adjust parameters like temperature and top_p for different styles of responses.
+
+   ```python
+   sequences = pipeline(
+         prompt,
+         do_sample=True,
+       temperature=0.7,
+          top_p=0.9,
+          num_return_sequences=1,
+          max_length=200,
+      )
+    print(sequences[0]['generated_text'])
+    ```
+
+This setup allows you to utilize the capabilities of the **bhavinjawade/SOLAR-10B-OrcaDPO-Jawade** model for generating responses to chat inputs.
+
+### License
+- **Type**: MIT License
+- **Details**: This license permits reuse, modification, and distribution for both private and commercial purposes under the terms of the MIT License.
+
+### Model Details
+- **Model Name**: SOLAR-10.7B-Instruct-v1.0
+- **Organization**: Upstage
+- **Training Dataset**: Intel/orca_dpo_pairs
+- **Technique Used**: LoRA (Low-Rank Adaptation)
+
+### Contact Information
+- https://bhavinjawade.github.io