# ArtifactAI/phi-2-arxiv-physics-instruct-GGUF

Quantized GGUF model files for [phi-2-arxiv-physics-instruct](https://huggingface.co/ArtifactAI/phi-2-arxiv-physics-instruct) from [ArtifactAI](https://huggingface.co/ArtifactAI)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [phi-2-arxiv-physics-instruct.fp16.gguf](https://huggingface.co/afrideva/phi-2-arxiv-physics-instruct-GGUF/resolve/main/phi-2-arxiv-physics-instruct.fp16.gguf) | fp16 | 5.56 GB  |
| [phi-2-arxiv-physics-instruct.q2_k.gguf](https://huggingface.co/afrideva/phi-2-arxiv-physics-instruct-GGUF/resolve/main/phi-2-arxiv-physics-instruct.q2_k.gguf) | q2_k | 1.17 GB  |
| [phi-2-arxiv-physics-instruct.q3_k_m.gguf](https://huggingface.co/afrideva/phi-2-arxiv-physics-instruct-GGUF/resolve/main/phi-2-arxiv-physics-instruct.q3_k_m.gguf) | q3_k_m | 1.48 GB  |
| [phi-2-arxiv-physics-instruct.q4_k_m.gguf](https://huggingface.co/afrideva/phi-2-arxiv-physics-instruct-GGUF/resolve/main/phi-2-arxiv-physics-instruct.q4_k_m.gguf) | q4_k_m | 1.79 GB  |
| [phi-2-arxiv-physics-instruct.q5_k_m.gguf](https://huggingface.co/afrideva/phi-2-arxiv-physics-instruct-GGUF/resolve/main/phi-2-arxiv-physics-instruct.q5_k_m.gguf) | q5_k_m | 2.07 GB  |
| [phi-2-arxiv-physics-instruct.q6_k.gguf](https://huggingface.co/afrideva/phi-2-arxiv-physics-instruct-GGUF/resolve/main/phi-2-arxiv-physics-instruct.q6_k.gguf) | q6_k | 2.29 GB  |



## Original Model Card:
#  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-2 model trained on [ArtifactAI/arxiv-physics-instruct-30k](https://huggingface.co/datasets/ArtifactAI/arxiv-physics-instruct-30k). 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-2-arxiv-physics-instruct"

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 is an atom?")
```

## Training Data

The model was trained on [ArtifactAI/arxiv-physics-instruct-50k](https://huggingface.co/datasets/ArtifactAI/arxiv-physics-instruct-50k), a dataset of question/answer pairs. Questions are generated using the t5-base model, while the answers are generated using the GPT-3.5-turbo model.

# Citation

```
@misc{phi-arxiv-physics-instruct,
    title={phi-2-arxiv-physics-instruct},
    author={Matthew Kenney},
    year={2023}
}
```
{"language": ["en"], "license": "apache-2.0", "tags": ["arxiv", "gguf", "ggml", "quantized", "q2_k", "q3_k_m", "q4_k_m", "q5_k_m", "q6_k"], "model_name": "phi-2-arxiv-physics-instruct", "base_model": "ArtifactAI/phi-2-arxiv-physics-instruct", "inference": false, "model_creator": "ArtifactAI", "pipeline_tag": "text-generation", "quantized_by": "afrideva", "widget": [{"example_title": "Question Answering", "text": "What is the peak phase of T-eV?"}]}
gguf arxiv ggml quantized q2_k q3_k_m q4_k_m q5_k_m q6_k text-generation en base_model:ArtifactAI/phi-2-arxiv-physics-instruct license:apache-2.0 region:us