--- 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 library_name: transformers --- # Open-Assistant Falcon 7B SFT OASST-TOP1 Model This model is a fine-tuning of TII's [Falcon 7B](https://huggingface.co/tiiuae/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](https://huggingface.co/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](https://wandb.ai/open-assistant/public-sft/runs/25apbcld) (Checkpoint: 696 steps) - **Code:** [Open-Assistant/model/model_training](https://github.com/LAION-AI/Open-Assistant/tree/main/model/model_training) - **Demo:** [Continuations for 250 random prompts](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Fchat-gpt%2F2023-04-11_gpt-3.5-turbo_lottery.json%0Ahttps%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-sft%2F2023-06-05_OpenAssistant_falcon-7b-sft-top1-696_sampling_noprefix2.json) - **License:** Apache 2.0 - **Contact:** [Open-Assistant Discord](https://ykilcher.com/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 ```python 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 --output_dir --deepspeed ``` Export command: ``` python export_model.py --dtype bf16 --hf_repo_name OpenAssistant/falcon-7b-sft-top1 --trust_remote_code --auth_token --max_shard_size 2GB ```