--- language: - en tags: - ctranslate2 - int8 - float16 - 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|> --- # # Fast-Inference with Ctranslate2 Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU. quantized version of [OpenAssistant/stablelm-7b-sft-v7-epoch-3](https://huggingface.co./OpenAssistant/stablelm-7b-sft-v7-epoch-3) ```bash pip install hf-hub-ctranslate2>=2.0.8 ctranslate2>=3.14.0 ``` Converted on 2023-06-02 using ``` ct2-transformers-converter --model OpenAssistant/stablelm-7b-sft-v7-epoch-3 --output_dir /home/michael/tmp-ct2fast-stablelm-7b-sft-v7-epoch-3 --force --copy_files tokenizer.json README.md tokenizer_config.json generation_config.json special_tokens_map.json .gitattributes --quantization int8_float16 --trust_remote_code ``` Checkpoint compatible to [ctranslate2>=3.14.0](https://github.com/OpenNMT/CTranslate2) and [hf-hub-ctranslate2>=2.0.8](https://github.com/michaelfeil/hf-hub-ctranslate2) - `compute_type=int8_float16` for `device="cuda"` - `compute_type=int8` for `device="cpu"` ```python from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub from transformers import AutoTokenizer model_name = "michaelfeil/ct2fast-stablelm-7b-sft-v7-epoch-3" # use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model. model = GeneratorCT2fromHfHub( # load in int8 on CUDA model_name_or_path=model_name, device="cuda", compute_type="int8_float16", # tokenizer=AutoTokenizer.from_pretrained("OpenAssistant/stablelm-7b-sft-v7-epoch-3") ) outputs = model.generate( text=["def fibonnaci(", "User: How are you doing? Bot:"], max_length=64, include_prompt_in_result=False ) print(outputs) ``` # Licence and other remarks: This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo. # Original description # Open-Assistant StableLM-7B SFT-7 Model This is the 7th iteration English supervised-fine-tuning (SFT) model of the [Open-Assistant](https://github.com/LAION-AI/Open-Assistant) project. It is based on a StableLM 7B that was fine-tuned on human demonstrations of assistant conversations collected through the [https://open-assistant.io/](https://open-assistant.io/) human feedback web app before April 12, 2023. ## Model Details - **Developed by:** [Open-Assistant Contributors](https://open-assistant.io/) - **Model type:** Transformer-based Language Model - **Language:** English - **Finetuned from:** [stabilityai/stablelm-base-alpha-7b](https://huggingface.co./stabilityai/stablelm-base-alpha-7b) - **Code:** [Open-Assistant/model/model_training](https://github.com/LAION-AI/Open-Assistant/tree/main/model/model_training) - **Demo:** TODO - **License:** Creative Commons license ([CC BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)) - **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. ## Dev Details - wandb: https://wandb.ai/open-assistant/supervised-finetuning/runs/08dfhyuc - base model: [stabilityai/stablelm-base-alpha-7b](https://huggingface.co./stabilityai/stablelm-base-alpha-7b) - checkpoint: 3 epochs (12000 steps) command: `deepspeed trainer_sft.py --configs defaults stablelm-7b oasst-mix --cache_dir /home/ubuntu/data_cache --output_dir .saved/stable-lm-7b-1 --num_train_epochs 4 --deepspeed` data: ``` oasst-mix: save_strategy: epoch sort_by_length: false use_custom_sampler: false datasets: - oasst_export: lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz - vicuna: val_split: 0.05 max_val_set: 800 fraction: 1.0 - dolly15k: val_split: 0.05 max_val_set: 300 - grade_school_math_instructions: val_split: 0.05 - code_alpaca: val_split: 0.05 max_val_set: 250 ``` stablelm: ``` stablelm-7b: dtype: fp16 log_dir: stablelm_log_7b model_name: stabilityai/stablelm-base-alpha-7b output_dir: stablelm_7b max_length: 4096 warmup_steps: 100 gradient_checkpointing: true gradient_accumulation_steps: 2 per_device_train_batch_size: 4 per_device_eval_batch_size: 4 eval_steps: 100 save_steps: 500 num_train_epochs: 4 save_total_limit: 4 use_flash_attention: true ``` zero config: ``` { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "bf16": { "enabled": "auto" }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } }, "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto", "total_num_steps": "auto" } }, "zero_optimization": { "stage": 2, "allgather_partitions": true, "allgather_bucket_size": 1e9, "overlap_comm": false, "reduce_scatter": true, "reduce_bucket_size": 1e9, "contiguous_gradients": true }, "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "steps_per_print": 2000, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "wall_clock_breakdown": false } ```