Open-Assistant Falcon 7B SFT OASST-TOP1 Model

This model is a fine-tuning of TII's Falcon 7B LLM. It was trained with 11,123 top-1 (high-quality) demonstrations of the OASST data set (exported on June 2, 2023) with a batch size of 128 for 8 epochs with LIMA style dropout (p=0.2) and a context-length of 2048 tokens.

Model Details

• Finetuned from: tiiuae/falcon-7b
• Model type: Causal decoder-only transformer language model
• Language: English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
• Weights & Biases: Training log (Checkpoint: 696 steps)
• Code: Open-Assistant/model/model_training
• Demo: Continuations for 250 random prompts
• License: Apache 2.0
• Contact: Open-Assistant Discord

Prompting

Two special tokens are used to mark the beginning of user and assistant turns: <|prompter|> and <|assistant|>. Each turn ends with a <|endoftext|> token.

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-top1-696"

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
 save_strategy: steps
 eval_steps: 80
 save_steps: 80
 warmup_steps: 20
 gradient_checkpointing: true
 gradient_accumulation_steps: 4
 per_device_train_batch_size: 4
 per_device_eval_batch_size: 8
 num_train_epochs: 8
 save_total_limit: 4
 residual_dropout: 0.2
 residual_dropout_lima: true

Dataset:

oasst-top1:
 # oasst_export: 11123 (100.00%)
 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: 1

Train command:

deepspeed trainer_sft.py --configs defaults falcon-7b oasst-top1 --cache_dir <data_cache_dir> --output_dir <output_path> --deepspeed

Export command:

python export_model.py --dtype bf16 --hf_repo_name OpenAssistant/falcon-7b-sft-top1 --trust_remote_code --auth_token <auth_token> <output_path> --max_shard_size 2GB