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--- |
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license: llama2 |
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language: |
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- en |
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datasets: |
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- rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored |
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- OpenAssistant/oasst1 |
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- ehartford/dolphin |
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- argilla/databricks-dolly-15k-curated-multilingual |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- sft |
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--- |
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# Open-Assistant Llama2 70B SFT v10 |
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This model is an Open-Assistant fine-tuning of Meta's [Llama2 70B](https://huggingface.co/meta-llama/Llama-2-70b) LLM. |
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It was fine-tuned in two stages, first on a mix of synthetic instrunctions and coding tasks and then in a "polishing" stage |
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on the best human demonstrations collected at [open-assistant.io](https://open-assistant.io/) up to July 23, 2023 (see [Configuration Details](#configuration-details) below). |
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## Model Details |
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- **Finetuned from:** [meta-llama/Llama-2-70b](https://huggingface.co/meta-llama/Llama-2-70b) via [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) |
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- **Model type:** Causal decoder-only transformer language model |
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- **Language:** English (and limited capabilities in German, Spanish, French, Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish) |
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- **Weights & Biases training logs:** [Stage 1](https://wandb.ai/open-assistant/public-sft/runs/run45_oasst_pre10_llama2_70b) (1 epoch pretrain-mix, 12k steps), [Stage 2](https://wandb.ai/open-assistant/public-sft/runs/run46_oasst_sft10_llama2_70b) (3 epochs oasst top-1, 519 steps) |
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- **Demo:** [Continuations for 250 random prompts (TGI, 4bit nf4 quantization)](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-sft%2F2023-08-22_OpenAssistant_llama2-70b-oasst-sft-v10_sampling_noprefix2_nf4.json%0A) |
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- **Evaluation** [FastEval-OpenAssistant Overview](https://tju01.github.io/FastEval-OpenAssistant/) (using [FastEval](https://github.com/FastEval/FastEval) & [vLLM](https://github.com/vllm-project/vllm)) |
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- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) |
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- **Contact:** [Open-Assistant Discord](https://ykilcher.com/open-assistant-discord) |
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## Prompting / Prompt Template |
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Due to public demand (see [survey](https://twitter.com/erhartford/status/1682403597525430272)) we changed the prompt-template for this model from custom prompter/assistant tokens to OpenAI's [chatml](https://github.com/openai/openai-python/blob/main/chatml.md) standard prompt format. |
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We hope that this leads to greater compatibility with chat inference/frontend applications. |
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Prompt dialogue template: |
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``` |
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""" |
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<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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""" |
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``` |
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The model input can contain multiple conversation turns between user and assistant, e.g. |
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``` |
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<|im_start|>user |
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{prompt 1}<|im_end|> |
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<|im_start|>assistant |
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{reply 1}<|im_end|> |
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<|im_start|>user |
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{prompt 2}<|im_end|> |
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<|im_start|>assistant |
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(...) |
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``` |
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The model was partly trained with orca system messages. |
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For inference we recommend to use the official [Llama2 system message](https://github.com/facebookresearch/llama/blob/ea9f33d6d3ea8ed7d560d270986407fd6c2e52b7/example_chat_completion.py#L57-L61): |
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``` |
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<|im_start|>system |
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You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. |
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If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. |
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<|im_end|> |
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``` |
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### Credits & Special Thanks |
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- Thanks to [Meta AI](https://ai.meta.com/) for training and releasing the Llama2 model. |
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- Compute was generously sponsored by the eplf [Machine Learning and Optimization Laboratory](https://www.epfl.ch/labs/mlo/). |
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- The open-source [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) trainer was used for fine-tuning. |
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- [rombodawg](https://huggingface.co/rombodawg) curated the [LosslessMegaCodeTrainingV2_1m_Evol_Uncensored](https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored) dataset. |
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- [ehartford](https://huggingface.co/ehartford) generated and published the [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) and the [ehartford/oa_leet10k](https://huggingface.co/datasets/ehartford/oa_leet10k) datasets. |
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- [Argilla](https://huggingface.co/argilla) curated and published the [argilla/databricks-dolly-15k-curated-multilingual] dataset. |
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- [shahules786](https://github.com/shahules786) de-duped and filtered the Dolphin dataset with a cluster-center approach and generated the orca-best (ocra-chat) dataset. |
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- [andreaskoepf](https://github.com/andreaskoepf/) prepared & orchestrated the training. |
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We want to especially thank everyone who contributed in the crowed-sourced Open-Assistant dataset creation on https://open-assistant.io/ - without you this project would not have been possible. |
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## Ethical Considerations and Limitations |
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Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. |
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For these reasons, as with all LLMs, the potential outputs of llama2-70b-oasst-sft-v10 cannot be predicted |
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in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses |
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to user prompts. Therefore, before deploying any applications of llama2-70b-oasst-sft-v10, developers should |
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perform safety testing and tuning tailored to their specific applications of the model. |
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Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/). |
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## Note regarding inference with TGI |
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During evaluation we noticed that this 70B model produced extremely poor outputs when loaded it was loaded in 16 bit precision sharded in [TGI](https://github.com/huggingface/text-generation-inference). |
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In contrast the model could be evaluated without problem using [vLLM](https://github.com/vllm-project/vllm). |
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The model also worked decently well when loaded with TGI on a single GPPU nf4 quantized via [TimDettmers/bitsandbytes](https://github.com/TimDettmers/bitsandbytes). |
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Will will get it touch with the TGI authors to find out why sharded 16-bit inference doesn't work as expected. |
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## Configuration Details |
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The "pretokenizer" utility used to tokenize the datamix is part of the Open-Assistant github repository and can be found here: [model/pretokenizer](https://github.com/LAION-AI/Open-Assistant/tree/main/model/pretokenizer). |
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### Stage 1 Pretokenizer Configuration |
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Entries of the dataset with assistant replies shorter than 25 tokens were excluded from training. |
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``` |
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oasst_pre10_min25: |
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datasets: |
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- megacode2: |
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fraction: 0.5 |
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val_split: 0.01 |
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max_val_set: 1000 |
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- orca-chat: |
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val_split: 0.01 |
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max_val_set: 1000 |
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- dolly15k_multilingual: |
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val_split: 0.05 |
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max_val_set: 300 |
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- oa_leet10k: |
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val_split: 0.05 |
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max_val_set: 250 |
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output_dir: "output/oasst_pre10_min25" |
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filename_prefix: "oasst_pre10" |
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min_assistant_tokens: 25 |
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``` |
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Stage 1 dataset statistics: |
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``` |
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# Stats for output/oasst_pre10_min25_llama2 |
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## Stats for 'Subset of InstructionDataset (megacode2)' (466364 samples (50.0%)) |
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----------------- |
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Accepted: 398223/466364 (85.4%) |
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Accepted tokens: 167676873 |
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Skipped: 68141 (14.6%) |
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Min tokens per sample: 36 |
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Max tokens per sample: 11810 |
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Avg tokens per sample: 421.063 |
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----------------- |
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## Stats for 'Subset of OrcaChat (orca-chat)' (325616 samples (100.0%)) |
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----------------- |
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Accepted: 325616/325616 (100.0%) |
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Accepted tokens: 178307574 |
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Skipped: 0 (0.0%) |
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Min tokens per sample: 105 |
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Max tokens per sample: 10408 |
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Avg tokens per sample: 547.601 |
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----------------- |
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## Stats for 'Subset of Dolly15kMultilingual' (57020 samples (100.0%)) |
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----------------- |
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Accepted: 47494/57020 (83.3%) |
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Accepted tokens: 13883177 |
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Skipped: 9526 (16.7%) |
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Min tokens per sample: 34 |
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Max tokens per sample: 9172 |
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Avg tokens per sample: 292.314 |
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----------------- |
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## Stats for 'Subset of InstructionDataset (oa_leet10k)' (22236 samples (100.0%)) |
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----------------- |
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Accepted: 22236/22236 (100.0%) |
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Accepted tokens: 15905296 |
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Skipped: 0 (0.0%) |
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Min tokens per sample: 168 |
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Max tokens per sample: 10588 |
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Avg tokens per sample: 715.295 |
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----------------- |
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## Stats for 'total' (871236 samples (100.0%)) |
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----------------- |
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Accepted: 793569/871236 (91.1%) |
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Accepted tokens: 375772920 |
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Skipped: 77667 (8.9%) |
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Min tokens per sample: 34 |
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Max tokens per sample: 11810 |
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Avg tokens per sample: 473.523 |
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----------------- |
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``` |
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### Stage 2 Pretokenizer Configuration |
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``` |
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oasst_top1: |
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datasets: |
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- oasst_export: |
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lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" |
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input_file_path: 2023-07-23_oasst_ready.tar.gz |
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top_k: 1 |
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val_split: 0.05 |
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output_dir: "output/oasst_top1_2023-07-23" |
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filename_prefix: "oasst_top1" |
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``` |
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Stage 2 dataset statistics: |
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``` |
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# Stats for output/oasst_top1_2023-07-23_llama2 |
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## Stats for 'ListDataset' (11441 samples (100.0%)) |
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----------------- |
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Accepted: 11441/11441 (100.0%) |
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Accepted tokens: 5315368 |
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Skipped: 0 (0.0%) |
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Min tokens per sample: 20 |
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Max tokens per sample: 5407 |
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Avg tokens per sample: 464.58945896337735 |
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----------------- |
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## Stats for 'total' (11441 samples (100.0%)) |
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----------------- |
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Accepted: 11441/11441 (100.0%) |
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Accepted tokens: 5315368 |
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Skipped: 0 (0.0%) |
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Min tokens per sample: 20 |
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Max tokens per sample: 5407 |
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Avg tokens per sample: 464.58945896337735 |
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----------------- |
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``` |
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### Megatron Fine-Tuning Arguments for Stage 1 (Instruction Tuning): |
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``` |
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--tensor_model_parallel_size 8 |
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--pipeline_model_parallel_size 4 |
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--load ./checkpoints/llama2-70b-tp8-pp4 |
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--save ./checkpoints/llama2-70b-tp8-pp4-oasst_pre10 |
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--tensorboard_dir ./checkpoints/llama2-70b-tp8-pp4-oasst_pre10/logging |
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--data_path ./data/oasst_pre10_min25_llama2/oasst_sft10-train |
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--model_name llama2 |
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--tokenizer_type SentencePieceTokenizer |
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--bf16 |
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--global_batch_size 64 |
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--micro_batch_size 2 |
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--vocab_file=./llama2/Llama-2-7b/tokenizer.model |
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--use_rms_norm |
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--glu_activation swiglu |
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--no_tie_embed_logits |
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--vocab_extra_ids_list "\"<|im_start|>,<|im_end|>\"" |
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--layernorm_epsilon 1e-5 |
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--use_flash_attn |
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--no_bias_gelu_fusion |
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--seq_length 4096 |
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--max_position_embeddings 4096 |
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--log_interval 1 |
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--save_interval 500 |
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--eval_interval 50 |
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--eval_iters 10 |
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--hidden_dropout 0.0 |
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--position_embedding_type rotary |
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--no_bias_dropout_fusion |
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--use_checkpoint_args |
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--train_iters 12000 |
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--attention_dropout 0.0 |
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--adam_beta1 0.9 |
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--adam_beta2 0.95 |
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--adam_eps 1e-12 |
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--lr_decay_style cosine |
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--lr_warmup_iters 100 |
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--lr 1e-5 |
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--min_lr 1e-6 |
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--weight_decay 0.000001 |
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--sequence_parallel |
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--recompute_granularity selective |
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--log_timers_to_tensorboard |
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--rope_scaling_factor 1.0 |
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--wandb_logger |
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``` |
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### Megatron Fine-Tuning Arguments for Stage 2 (OASST Polishing, LIMA Dropout): |
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``` |
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--tensor_model_parallel_size 8 |
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--pipeline_model_parallel_size 4 |
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--load ./checkpoints/llama2-70b-tp8-pp4-oasst_pre10 |
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--save ./checkpoints/llama2-70b-tp8-pp4-oasst_sft10 |
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--tensorboard_dir ./checkpoints/llama2-70b-tp8-pp4-oasst_sft10/logging |
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--data_path ./data/oasst_top1_2023-07-23_llama2/oasst_top1-train |
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--model_name llama2 |
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--tokenizer_type SentencePieceTokenizer |
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--bf16 |
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--global_batch_size 64 |
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--micro_batch_size 2 |
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--vocab_file=./llama2/Llama-2-7b/tokenizer.model |
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--use_rms_norm |
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--glu_activation swiglu |
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--no_tie_embed_logits |
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--vocab_extra_ids_list "\"<|im_start|>,<|im_end|>\"" |
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--layernorm_epsilon 1e-5 |
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--use_flash_attn |
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--no_bias_gelu_fusion |
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--seq_length 4096 |
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--max_position_embeddings 4096 |
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--log_interval 1 |
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--save_interval 346 |
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--eval_interval 50 |
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--eval_iters 10 |
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--hidden_dropout 0.25 |
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--lima_dropout |
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--position_embedding_type rotary |
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--no_bias_dropout_fusion |
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--use_checkpoint_args |
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--train_iters 519 |
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--attention_dropout 0.0 |
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--adam_beta1 0.9 |
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--adam_beta2 0.95 |
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--adam_eps 1e-12 |
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--lr_decay_style cosine |
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--lr_warmup_iters 100 |
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--lr 1e-5 |
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--min_lr 1e-6 |
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--weight_decay 0.000001 |
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--sequence_parallel |
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--recompute_granularity selective |
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--log_timers_to_tensorboard |
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--rope_scaling_factor 1.0 |
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--finetune |
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--wandb_logger |
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``` |
