Create README.md

README.md

@@ -0,0 +1,110 @@
+---
+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/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 [camel-ai/phi-physics](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}
+}
+```