metadata

license: cc-by-nc-4.0
datasets:
  - conceptofmind/cot_submix_original
  - conceptofmind/flan2021_submix_original
  - conceptofmind/t0_submix_original
  - conceptofmind/niv2_submix_original
language:
  - en
pipeline_tag: text-generation

FreeWilly

Model Description

FreeWilly is a Llama65B model finetuned on an Orca style Dataset

Usage

Apply Delta Weights

FreeWilly1 cannot be used from the stabilityai/FreeWilly1-Delta-SafeTensor weights alone. To obtain the correct model, one must add back the difference between LLaMA 65B and stabilityai/FreeWilly1-Delta-SafeTensor weights. We provide the apply_delta.py script to automate the conversion, which you can run as:

python3 apply_delta.py --base /path/to/model_weights/llama-65b --target FreeWilly1 --delta stabilityai/FreeWilly1-Delta-SafeTensor

Start chatting with FreeWilly using the following code snippet:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("your_path_to_freewilly", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("your_path_to_freewilly", torch_dtype=torch.float16, low_cpu_mem_usage=True, use_accelerate=True)
generator = pipeline(model=model, tokenizer=tokenizer)
system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
system_prompt += "### Instruction:\nYou are Free Willy, an AI that follows instructions extremely well. Help as much as you can. Remember, be safe, and don't do anything illegal.\n\n"

message = "Write me a poem please"
prompt = f"{system_prompt}### Input: {message}\n\n### Response:\n"

output = generator(prompt, num_return_sequences=1, do_sample=True, top_p=0.95, top_k=0)
print(output)

FreeWilly should be used with prompts formatted similarly to Alpaca as below:

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

## Instruction:
This is a system prompt, please behave and help the user.

### Input:
Your prompt here

### Response
The output of FreeWilly

Model Details

Developed by: Stability AI
Model type: FreeWilly is an auto-regressive language model fine-tuned on LLaMA65B.
Language(s): English
Library: HuggingFace Transformers
License: Fine-tuned checkpoints (FreeWilly) is licensed under the Non-Commercial Creative Commons license (CC BY-NC-4.0)
Contact: For questions and comments about the model, please email [email protected]

Training Dataset

FreeWilly is trained on our internal Orca-style dataset

Training Procedure

Models are learned via supervised fine-tuning on the aforementioned datasets, trained in mixed-precision (BF16), and optimized with AdamW. We outline the following hyperparameters:

Dataset	Batch Size	Learning Rate	Learning Rate Decay	Warm-up	Weight Decay	Betas
Orca pt1 packed	512	3e-5	Cosine to 3e-6	100	1e-6	(0.9, 0.95)
Orca pt2 unpacked	512	3e-5	Cosine to 3e-6	100	1e-6	(0.9, 0.95)

Use and Limitations

Intended Use

These models are intended for research only, in adherence with the CC BY-NC-4.0 license.

Limitations and bias

Although the aforementioned dataset helps to steer the base language models into "safer" distributions of text, not all biases and toxicity can be mitigated through fine-tuning. We ask that users be mindful of such potential issues that can arise in generated responses. Do not treat model outputs as substitutes for human judgment or as sources of truth. Please use it responsibly.

Citations

@misc{touvron2023llama,
      title={LLaMA: Open and Efficient Foundation Language Models}, 
      author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
      year={2023},
      eprint={2302.13971},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{mukherjee2023orca,
      title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, 
      author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
      year={2023},
      eprint={2306.02707},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{alpaca,
  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
  title = {Stanford Alpaca: An Instruction-following LLaMA model},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}