transformers safetensors phi text-generation arxiv summarization custom_code en license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
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 [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA). 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-metamath"

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 [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA), 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-metamath,
    title={phi-metamath},
    author={Matthew Kenney},
    year={2023}
}
```