Phi Hermes 1.3B

🚀 Model Card for Puffin-Phi V2

Phi-1.5 fine tuned with Hermes Dataset, offering enhanced text generation capabilities.

🚀 Quick Start

Phi does not support device_map "auto", and does not seem to want to inference in fp16, so use bf16.

Here is working code to inference, though it can be improved:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("teknium/Puffin-Phi-v2", trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("teknium/Puffin-Phi-v2", trust_remote_code=True, torch_dtype=torch.bfloat16)
inputs = tokenizer(f"### Instruction:\nWrite a negative review for the website, Twitter.\n### Response:\n", return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=128, do_sample=True, temperature=0.2, top_p=0.9, use_cache=True, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)
text = tokenizer.batch_decode(outputs)[0]
print(text)

The prompt format is Alpaca, then is prompted like so:

### Instruction:
<prompt>
### Response:

✨ Features

This model was fine - tuned from Phi - 1.5 with the Hermes Dataset, leveraging a large number of synthetic datapoints.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("teknium/Puffin-Phi-v2", trust_remote_code=True, torch_dtype=torch.bfloat16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained("teknium/Puffin-Phi-v2", trust_remote_code=True, torch_dtype=torch.bfloat16)
inputs = tokenizer(f"### Instruction:\nWrite a negative review for the website, Twitter.\n### Response:\n", return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=128, do_sample=True, temperature=0.2, top_p=0.9, use_cache=True, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)
text = tokenizer.batch_decode(outputs)[0]
print(text)

📚 Documentation

Model Details

Model Sources

This model was trained on the OpenHermes Dataset, made by me, which is over 240,000 mostly GPT - 4 generated synthetic datapoints

Uses

Let me know!

Training Details

Training Procedure

Trained with Axolotl. View the wandb runs for all my puffin runs (this is puffin - phi - 4 on wandb): https://wandb.ai/teknium1/hermes-phi/runs/hermes-phi-1

Evaluation

📄 License

License: other