Open-Assistant Falcon 40B SFT OASST-TOP1 Model
This model is a fine-tuning of TII's Falcon 40B LLM. It was trained with top-1 (high-quality) demonstrations of the OASST data set (exported on May 6, 2023) with an effective batch size of 144 for ~7.5 epochs with LIMA style dropout (p=0.3) and a context-length of 2048 tokens.
Model Details
- Finetuned from: [tiiuae/falcon-40b]((https://huggingface.co/tiiuae/falcon-40b)
- Model type: Causal decoder-only transformer language model
- Language: English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
- Demo: Continuations for 250 random prompts
- Eval results: ilm-eval
- Weights & Biases: Training log (Checkpoint: 560 steps)
- 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.
Configuration Details
Model:
falcon-40b:
dtype: bf16
log_dir: "falcon_log_40b"
learning_rate: 5e-6
model_name: "tiiuae/falcon-40b"
deepspeed_config: configs/zero3_config_falcon.json
output_dir: falcon
weight_decay: 0.0
max_length: 2048
warmup_steps: 20
gradient_checkpointing: true
gradient_accumulation_steps: 1
per_device_train_batch_size: 18
per_device_eval_batch_size: 10
eval_steps: 80
save_steps: 80
num_train_epochs: 8
save_total_limit: 4
use_flash_attention: false
residual_dropout: 0.3
residual_dropout_lima: true
sort_by_length: false
save_strategy: steps
Dataset:
oasst-top1:
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: 2023-05-06_OASST_labels.jsonl.gz
val_split: 0.05
top_k: 1