RichardErkhov/

soulhq-ai_-_phi-2-insurance_qa-sft-lora-gguf

• Model creator: https://huggingface.co/soulhq-ai/
• Original model: https://huggingface.co/soulhq-ai/phi-2-insurance_qa-sft-lora/

Original model description:

• en pipeline_tag: text-generation tags:
• nlp
• insurance
• lora
• llm
• phi-2
• transformers
• qa
• sft

• soulhq-ai/insuranceQA-v2 widget:
• text: "### Instruction: What is the difference between health and life insurance?\n#### Response: "
• text: "### Instruction: Does Homeowners Insurance Cover Death Of Owner?\n#### Response: "

Model Summary

Dataset

Usage

• When loading the model, ensure that trust_remote_code=True is passed as an argument of the from_pretrained() function.
• Update your local transformers to the development version: pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers. The previous command is an alternative to cloning and installing from the source.

Input Format

### Instruction: <Question>
### Response: 

### Instruction: What does Basic Homeowners Insurance Cover?
### Response: 

Inference Code

Training

Model

• Architecture: Phi-2, with LoRA modifications for efficient Insurance domain-specific fine-tuning.
• Context length: 2048 tokens
• Modifications: Added <|eostoken|> for end-of-response learning - to help the model learn the end of responses, facilitating its use in dialogue systems.

Configuration

• Hyperparameters:
  • learning_rate=2e-5,
  • batch_size=8,
  • epochs=10,
  • lora_r=32,
  • lora_alpha=64.
• Infrastructure: Trained on an NVIDIA A40 and utilized the FullyShardedDataParallelPlugin for CPU offloading.

Evaluation

Limitations of soulhq-ai/phi-2-insurance_qa-sft-lora

• Generate Inaccurate Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
• Unreliable Responses to Instruction: It may struggle or fail to adhere to intricate or nuanced instructions provided by users.
• Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other languages might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
• Potential Societal Biases: Phi-2 is not entirely free from societal biases despite efforts in assuring training data safety. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
• Toxicity: Despite being trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.
• Verbosity: Phi-2 being a base model often produces irrelevant or extra text and responses following its first answer to user prompts within a single turn. This is due to its training dataset being primarily textbooks, which results in textbook-like responses.

License

Citations