| --- |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: summarization |
| widget: |
| - text: What is the peak phase of T-eV? |
| example_title: Question Answering |
| tags: |
| - arxiv |
| --- |
| # Table of Contents |
|
|
| 0. [TL;DR](#TL;DR) |
| 1. [Model Details](#model-details) |
| 2. [Usage](#usage) |
| 3. [Uses](#uses) |
| 4. [Citation](#citation) |
|
|
| # TL;DR |
|
|
| This is a Phi-1_5 model trained on [camel-ai/physics](https://huggingface.co/datasets/camel-ai/physics). This model is for research purposes only and ***should not be used in production settings***. |
| |
| |
| ## Model Description |
| |
| |
| - **Model type:** Language model |
| - **Language(s) (NLP):** English |
| - **License:** Apache 2.0 |
| - **Related Models:** [Phi-1_5](https://huggingface.co/microsoft/phi-1_5) |
| |
| # Usage |
| |
| Find below some example scripts on how to use the model in `transformers`: |
| |
| ## Using the Pytorch model |
| |
| ```python |
| |
| from huggingface_hub import notebook_login |
| from datasets import load_dataset, Dataset |
| from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
|
|
| model = "ArtifactAI/phi-physics" |
|
|
| model = AutoModelForCausalLM.from_pretrained(base_model, trust_remote_code= True) |
| tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True) |
|
|
| def generate(prompt): |
| inputs = tokenizer(f'''Below is an instruction that describes a task. Write a response that appropriately completes the request If you are adding additional white spaces, stop writing".\n\n### Instruction:\n{prompt}.\n\n### Response:\n ''', return_tensors="pt", return_attention_mask=False) |
| streamer = TextStreamer(tokenizer, skip_prompt= True) |
| _ = model.generate(**inputs, streamer=streamer, max_new_tokens=500) |
| |
| generate("What are the common techniques used in identifying a new species, and how can scientists accurately categorize it within the existing taxonomy system?") |
| ``` |
| |
| ## Training Data |
| |
| The model was trained on [camel-ai/phi-physics](https://huggingface.co/datasets/camel-ai/physics), a dataset of question/answer pairs. |
| |
| |
| ## Training procedure |
| |
| |
| The following `bitsandbytes` quantization config was used during training: |
| - quant_method: bitsandbytes |
| - load_in_8bit: False |
| - load_in_4bit: True |
| - llm_int8_threshold: 6.0 |
| - llm_int8_skip_modules: None |
| - llm_int8_enable_fp32_cpu_offload: False |
| - llm_int8_has_fp16_weight: False |
| - bnb_4bit_quant_type: nf4 |
| - bnb_4bit_use_double_quant: True |
| - bnb_4bit_compute_dtype: float16 |
| |
| ### Framework versions |
| |
| |
| - PEFT 0.6.2 |
| ## Training procedure |
| |
| |
| The following `bitsandbytes` quantization config was used during training: |
| - quant_method: bitsandbytes |
| - load_in_8bit: False |
| - load_in_4bit: True |
| - llm_int8_threshold: 6.0 |
| - llm_int8_skip_modules: None |
| - llm_int8_enable_fp32_cpu_offload: False |
| - llm_int8_has_fp16_weight: False |
| - bnb_4bit_quant_type: nf4 |
| - bnb_4bit_use_double_quant: True |
| - bnb_4bit_compute_dtype: float16 |
| |
| ### Framework versions |
| |
| |
| - PEFT 0.6.2 |
| |
| # Citation |
| |
| ``` |
| @misc{phi-math, |
| title={phi-physics}, |
| author={Matthew Kenney}, |
| year={2023} |
| } |
| ``` |
| |
