metadata

datasets:
  - OpenAssistant/oasst1
pipeline_tag: text-generation
license: apache-2.0

🚀 Falcon-7b-chat-oasst1

Falcon-7b-chat-oasst1 is a chatbot-like model for dialogue generation. It was built by fine-tuning Falcon-7B on the OpenAssistant/oasst1 dataset. This repo only includes the LoRA adapters from fine-tuning with 🤗's peft package.

Model Summary

Model Type: Causal decoder-only
Language(s): English
Base Model: Falcon-7B (License: Apache 2.0)
Dataset: OpenAssistant/oasst1 (License: Apache 2.0)
License(s): Apache 2.0 inherited from "Base Model" and "Dataset"

Model Details

The model was fine-tuned in 8-bit precision using 🤗 peft adapters, transformers, and bitsandbytes. Training relied on a method called "Low Rank Adapters" (LoRA), specifically the QLoRA variant. The run took approximately 6.25 hours and was executed on a workstation with a single A100-SXM NVIDIA GPU with 37 GB of available memory. See attached Colab Notebook for the code and hyperparams used to train the model.

Model Date

May 30, 2023

Quick Start

To prompt the chat model, use the following format:

<human>: [Instruction]
<bot>:

Example Dialogue 1

Prompter:

"""<human>: My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB.
<bot>:"""

Falcon-7b-chat-oasst1:

Dear friends,

I am so excited to host a dinner party at my home this Friday! I will be making a delicious meal, but I would love for you to bring your favorite bottle of wine to share with everyone.

Please let me know if you can make it and if you have any dietary restrictions I should be aware of. I look forward to seeing you soon!

Best,
Daniel

Example Dialogue 2

Prompter:

<human>: Create a list of things to do in San Francisco.
<bot>:

Falcon-7b-chat-oasst1:

Here are some things to do in San Francisco:

1. Visit the Golden Gate Bridge
2. Explore the city's many museums and art galleries
3. Take a walk along the Embarcadero waterfront
4. Enjoy the views from the top of Coit Tower
5. Shop at Union Square and the Ferry Building
6. Eat at one of the city's many restaurants and cafes
7. Attend a sporting event at AT&T Park
8. Visit the Castro District and the Mission District
9. Take a day trip to Napa Valley or Muir Woods National Monument
10. Explore the city's many parks and gardens

Direct Use

This model has been finetuned on conversation trees from OpenAssistant/oasst1 and should only be used on data of a similar nature.

Out-of-Scope Use

Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.

Bias, Risks, and Limitations

This model is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.

Recommendations

We recommend users of this model to develop guardrails and to take appropriate precautions for any production use.

How to Get Started with the Model

Setup

# Install packages
!pip install -q -U bitsandbytes loralib einops
!pip install -q -U git+https://github.com/huggingface/transformers.git 
!pip install -q -U git+https://github.com/huggingface/peft.git
!pip install -q -U git+https://github.com/huggingface/accelerate.git

GPU Inference in 8-bit

This requires a GPU with at least 12 GB of memory.

First, Load the Model

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

# load the model
peft_model_id = "dfurman/falcon-7b-chat-oasst1"
config = PeftConfig.from_pretrained(peft_model_id)

model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    return_dict=True,
    device_map={"":0},
    trust_remote_code=True,
    load_in_8bit=True,
)

tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
tokenizer.pad_token = tokenizer.eos_token

model = PeftModel.from_pretrained(model, peft_model_id)

Next, Run the Model

prompt = """<human>: My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB.
<bot>:"""

batch = tokenizer(
    prompt,
    padding=True,
    truncation=True,
    return_tensors='pt'
)
batch = batch.to('cuda:0')

with torch.cuda.amp.autocast():
    output_tokens = model.generate(
        inputs=batch.input_ids, 
        max_new_tokens=200,
        do_sample=False,
        use_cache=True,
        temperature=1.0,
        top_k=50,
        top_p=1.0,
        num_return_sequences=1,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
        bos_token_id=tokenizer.eos_token_id,
    )

generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
# Inspect message response in the outputs
print(generated_text.split("<human>: ")[1].split("<bot>: ")[-1])

Reproducibility

See attached Colab Notebook for the code (and hyperparams) used to train the model.

CUDA Info

CUDA Version: 12.0
Hardware: 1 A100-SXM
Max Memory: {0: "37GB"}
Device Map: {"": 0}

Package Versions Employed

torch: 2.0.1+cu118
transformers: 4.30.0.dev0
peft: 0.4.0.dev0
accelerate: 0.19.0
bitsandbytes: 0.39.0
einops: 0.6.1