78 lines
2.7 KiB
Markdown
78 lines
2.7 KiB
Markdown
---
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license: mit
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datasets:
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- Intel/orca_dpo_pairs
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---
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## SOLAR-10B-OrcaDPO-Jawade
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### Overview
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This model card is instruction finetuned version of `upstage/SOLAR-10.7B-Instruct-v1.0` model. Trained on the Intel DPO Orca dataset using LoRA. Though it should be noted SOLAR-10.7B paper states that the
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original model for alignment was trained on Intel ORCA DPO pairs. Retraining using DPO and LoRA shows slight (<1%) improvement on OpenLLM Leaderboard benchmarks against `SOLAR 10.7B-Instruct` and significant over `SOLAR 10.7B`
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## How to Use This Model
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To use the model `bhavinjawade/SOLAR-10B-OrcaDPO-Jawade`, follow these steps:
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1. **Import and Load the Model and Tokenizer**
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Begin by importing the model and tokenizer. Load them using the `from_pretrained` method.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade")
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tokenizer = AutoTokenizer.from_pretrained("bhavinjawade/SOLAR-10B-OrcaDPO-Jawade")
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```
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2. **Format the Prompt**
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Format the chat input as a list of messages, each with a role ('system' or 'user') and content.
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```python
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message = [
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{"role": "system", "content": "You are a helpful assistant chatbot."},
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{"role": "user", "content": "Is the universe real? or is it a simulation? whats your opinion?"}
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]
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prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
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```
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3. **Create a Pipeline**
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Set up a pipeline for text generation with the loaded model and tokenizer.
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```python
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer
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)
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```
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4. **Generate Text**
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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.
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```python
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sequences = pipeline(
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prompt,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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num_return_sequences=1,
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max_length=200,
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)
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print(sequences[0]['generated_text'])
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```
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This setup allows you to utilize the capabilities of the **bhavinjawade/SOLAR-10B-OrcaDPO-Jawade** model for generating responses to chat inputs.
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### License
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- **Type**: MIT License
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- **Details**: This license permits reuse, modification, and distribution for both private and commercial purposes under the terms of the MIT License.
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### Model Details
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- **Model Name**: SOLAR-10.7B-Instruct-v1.0
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- **Organization**: Upstage
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- **Training Dataset**: Intel/orca_dpo_pairs
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- **Technique Used**: LoRA (Low-Rank Adaptation)
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### Contact Information
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- https://bhavinjawade.github.io |