metadata

license: apache-2.0
language:
  - en
  - de
  - es
  - fr
tags:
  - sft
pipeline_tag: text-generation
widget:
  - text: >-
      <|prompter|>What is a meme, and what's the history behind this
      word?<|endoftext|><|assistant|>
  - text: <|prompter|>What's the Earth total population<|endoftext|><|assistant|>
  - text: >-
      <|prompter|>Write a story about future of AI
      development<|endoftext|><|assistant|>
datasets:
  - OpenAssistant/oasst1

Open-Assistant Falcon 7B SFT MIX Model

base model: tiiuae/falcon-7b
sampling report
wandb: https://wandb.ai/open-assistant/public-sft/runs/tlevhltw
checkpoint: 2000 steps (~2.9 epochs)

Prompting

Input prompt example:

<|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|>

The input ends with the <|assistant|> token to signal that the model should start generating the assistant reply.

Sample Code

from transformers import AutoTokenizer
import transformers
import torch

model = "OpenAssistant/falcon-7b-sft-mix-2000"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)

input_text="<|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|>"

sequences = pipeline(
    input_text,
    max_length=500,
    do_sample=True,
    return_full_text=False,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Configuration Details

Model:

falcon-7b:
  dtype: bf16
  log_dir: "falcon_log_7b"
  learning_rate: 1e-5
  model_name: "tiiuae/falcon-7b"
  deepspeed_config: configs/zero_config.json
  output_dir: falcon
  weight_decay: 0.0
  max_length: 2048
  warmup_steps: 20
  gradient_checkpointing: true
  gradient_accumulation_steps: 4
  per_device_train_batch_size: 4
  per_device_eval_batch_size: 8
  eval_steps: 100
  save_steps: 500
  save_strategy: steps
  num_train_epochs: 8
  save_total_limit: 4
  residual_dropout: 0.2
  residual_dropout_lima: true

Dataset:

sft9-stage2:
  # oasst_export: 100.00% (29899)
  # vicuna: 50.00% (16963)
  # code_alpaca: 50.00% (9510)
  # oa_wiki_qa_bart_10000row: 100.00% (9434)
  # grade_school_math_instructions: 100.00% (8351)
  # dolly15k: 100.00% (14250)

  use_custom_sampler: true
  datasets:
    - oasst_export:
        lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" # sft-8.0
        input_file_path: 2023-06-02_oasst_all_labels.jsonl.gz
        val_split: 0.05
        top_k: 2
    - vicuna:
        fraction: 0.5
        val_split: 0.025
        max_val_set: 250
    - code_alpaca:
        fraction: 0.5
        val_split: 0.05
        max_val_set: 250
    - oa_wiki_qa_bart_10000row:
        val_split: 0.05
        max_val_set: 250
    - grade_school_math_instructions:
        val_split: 0.05
    - dolly15k:
        val_split: 0.05
        max_val_set: 300