README.md

---
datasets:
- rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored
- OpenAssistant/oasst1
- ehartford/dolphin
- argilla/databricks-dolly-15k-curated-multilingual
inference: false
language:
- en
library_name: transformers
license: llama2
model_creator: OpenAssistant
model_link: https://huggingface.co/OpenAssistant/llama2-70b-oasst-sft-v10
model_name: Llama2 70B SFT v10
model_type: llama
pipeline_tag: text-generation
quantized_by: TheBloke
tags:
- sft
---

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# Llama2 70B SFT v10 - GGUF
- Model creator: [OpenAssistant](https://huggingface.co/OpenAssistant)
- Original model: [Llama2 70B SFT v10](https://huggingface.co/OpenAssistant/llama2-70b-oasst-sft-v10)

## Description

This repo contains GGUF format model files for [OpenAssistant's Llama2 70B SFT v10](https://huggingface.co/OpenAssistant/llama2-70b-oasst-sft-v10).

<!-- README_GGUF.md-about-gguf start -->
### About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

The key benefit of GGUF is that it is a extensible, future-proof format which stores more information about the model as metadata. It also includes significantly improved tokenization code, including for the first time full support for special tokens. This should improve performance, especially with models that use new special tokens and implement custom prompt templates.

As of August 25th, here is a list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp)
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI. Supports GGUF with GPU acceleration via the ctransformers backend - llama-cpp-python backend should work soon too.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), now supports GGUF as of release 1.41! A powerful GGML web UI, with full GPU accel. Especially good for story telling.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), should now work, choose the `c_transformers` backend. A great web UI with many interesting features. Supports CUDA GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), now supports GGUF as of version 0.2.24! A Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), supports GGUF as of version 0.1.79. A Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), added GGUF support on August 22nd. Candle is a Rust ML framework with a focus on performance, including GPU support, and ease of use.

The clients and libraries below are expecting to add GGUF support shortly:
* [LM Studio](https://lmstudio.ai/), should be updated by end August 25th.
<!-- README_GGUF.md-about-gguf end -->

<!-- repositories-available start -->
## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GGUF)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GGML)
* [OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenAssistant/llama2-70b-oasst-sft-v10)
<!-- repositories-available end -->

<!-- prompt-template start -->
## Prompt template: ChatML

```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility

These quantised GGUF files are compatible with llama.cpp from August 21st 2023 onwards, as of commit [6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9](https://github.com/ggerganov/llama.cpp/commit/6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9)

As of August 24th 2023 they are now compatible with KoboldCpp, release 1.41 and later.

They are are not yet compatible with any other third-party UIS, libraries or utilities but this is expected to change very soon.

## Explanation of quantisation methods
<details>
  <summary>Click to see details</summary>

The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->

<!-- README_GGUF.md-provided-files start -->
## Provided files

| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama2-70b-oasst-sft-v10.Q2_K.gguf](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GGUF/blob/main/llama2-70b-oasst-sft-v10.Q2_K.gguf) | Q2_K | 2 | 29.48 GB| 31.98 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama2-70b-oasst-sft-v10.Q3_K_S.gguf](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GGUF/blob/main/llama2-70b-oasst-sft-v10.Q3_K_S.gguf) | Q3_K_S | 3 | 30.09 GB| 32.59 GB | very small, high quality loss |
| [llama2-70b-oasst-sft-v10.Q3_K_M.gguf](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GGUF/blob/main/llama2-70b-oasst-sft-v10.Q3_K_M.gguf) | Q3_K_M | 3 | 33.45 GB| 35.95 GB | very small, high quality loss |
| [llama2-70b-oasst-sft-v10.Q3_K_L.gguf](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GGUF/blob/main/llama2-70b-oasst-sft-v10.Q3_K_L.gguf) | Q3_K_L | 3 | 36.49 GB| 38.99 GB | small, substantial quality loss |
| [llama2-70b-oasst-sft-v10.Q4_K_S.gguf](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GGUF/blob/main/llama2-70b-oasst-sft-v10.Q4_K_S.gguf) | Q4_K_S | 4 | 39.30 GB| 41.80 GB | small, greater quality loss |
| [llama2-70b-oasst-sft-v10.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GGUF/blob/main/llama2-70b-oasst-sft-v10.Q4_K_M.gguf) | Q4_K_M | 4 | 41.69 GB| 44.19 GB | medium, balanced quality - recommended |
| [llama2-70b-oasst-sft-v10.Q5_K_S.gguf](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GGUF/blob/main/llama2-70b-oasst-sft-v10.Q5_K_S.gguf) | Q5_K_S | 5 | 47.74 GB| 50.24 GB | large, low quality loss - recommended |
| [llama2-70b-oasst-sft-v10.Q5_K_M.gguf](https://huggingface.co/TheBloke/Llama2-70B-OASST-SFT-v10-GGUF/blob/main/llama2-70b-oasst-sft-v10.Q5_K_M.gguf) | Q5_K_M | 5 | 49.03 GB| 51.53 GB | large, very low quality loss - recommended |
| llama2-70b-oasst-sft-v10.Q6_K.bin | q6_K | 6 | 56.82 GB | 59.32 GB | very large, extremely low quality loss |
| llama2-70b-oasst-sft-v10.Q8_0.bin | q8_0 | 8 | 73.29 GB | 75.79 GB | very large, extremely low quality loss - not recommended |

**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

### Q6_K and Q8_0 files are split and require joining

**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.

<details>
  <summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
   
### q6_K 
Please download:
* `llama2-70b-oasst-sft-v10.Q6_K.gguf-split-a`
* `llama2-70b-oasst-sft-v10.Q6_K.gguf-split-b`

### q8_0
Please download:
* `llama2-70b-oasst-sft-v10.Q8_0.gguf-split-a`
* `llama2-70b-oasst-sft-v10.Q8_0.gguf-split-b`

To join the files, do the following:

Linux and macOS:
```
cat llama2-70b-oasst-sft-v10.Q6_K.gguf-split-* > llama2-70b-oasst-sft-v10.Q6_K.gguf && rm llama2-70b-oasst-sft-v10.Q6_K.gguf-split-*
cat llama2-70b-oasst-sft-v10.Q8_0.gguf-split-* > llama2-70b-oasst-sft-v10.Q8_0.gguf && rm llama2-70b-oasst-sft-v10.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B llama2-70b-oasst-sft-v10.Q6_K.gguf-split-a + llama2-70b-oasst-sft-v10.Q6_K.gguf-split-b llama2-70b-oasst-sft-v10.Q6_K.gguf
del llama2-70b-oasst-sft-v10.Q6_K.gguf-split-a llama2-70b-oasst-sft-v10.Q6_K.gguf-split-b

COPY /B llama2-70b-oasst-sft-v10.Q8_0.gguf-split-a + llama2-70b-oasst-sft-v10.Q8_0.gguf-split-b llama2-70b-oasst-sft-v10.Q8_0.gguf
del llama2-70b-oasst-sft-v10.Q8_0.gguf-split-a llama2-70b-oasst-sft-v10.Q8_0.gguf-split-b
```

</details>

<!-- README_GGUF.md-provided-files end -->

<!-- README_GGUF.md-how-to-run start -->
## How to run in `llama.cpp`

Make sure you are using `llama.cpp` from commit [6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9](https://github.com/ggerganov/llama.cpp/commit/6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9) or later.

For compatibility with older versions of llama.cpp, or for use with third-party clients and libaries, please use GGML files instead.

```
./main -t 10 -ngl 32 -m llama2-70b-oasst-sft-v10.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.

Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change `-c 4096` to the desired sequence length for this model. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.

If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)

## How to run in `text-generation-webui`

Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
<!-- README_GGUF.md-how-to-run end -->

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## Thanks, and how to contribute.

Thanks to the [chirper.ai](https://chirper.ai) team!

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If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

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Thank you to all my generous patrons and donaters!

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<!-- original-model-card start -->
# Original model card: OpenAssistant's Llama2 70B SFT v10

# Open-Assistant Llama2 70B SFT v10

This model is an Open-Assistant fine-tuning of Meta's [Llama2 70B](https://huggingface.co/meta-llama/Llama-2-70b) LLM. 
It was fine-tuned in two stages, first on a mix of synthetic instrunctions and coding tasks and then in a "polishing" stage
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).

## Model Details

- **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)
- **Model type:** Causal decoder-only transformer language model
- **Language:** English (and limited capabilities in German, Spanish, French, Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish)
- **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)
- **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)
- **Evaluation** [FastEval-OpenAssistant Overview](https://tju01.github.io/FastEval-OpenAssistant/) (using [FastEval](https://github.com/FastEval/FastEval) & [vLLM](https://github.com/vllm-project/vllm)) 
- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
- **Contact:** [Open-Assistant Discord](https://ykilcher.com/open-assistant-discord)


## Prompting / Prompt Template

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.
We hope that this leads to greater compatibility with chat inference/frontend applications.

Prompt dialogue template:

```
"""
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
"""
```

The model input can contain multiple conversation turns between user and assistant, e.g.
```
<|im_start|>user
{prompt 1}<|im_end|>
<|im_start|>assistant
{reply 1}<|im_end|>
<|im_start|>user
{prompt 2}<|im_end|>
<|im_start|>assistant
(...)
```

The model was partly trained with orca system messages.  
For inference we recommend to use the official [Llama2 system message](https://github.com/facebookresearch/llama/blob/ea9f33d6d3ea8ed7d560d270986407fd6c2e52b7/example_chat_completion.py#L57-L61):
```
<|im_start|>system
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.

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.
<|im_end|>
```

### Credits & Special Thanks

- Thanks to [Meta AI](https://ai.meta.com/) for training and releasing the Llama2 model.
- Distributed training support was provided by EPFL's [Machine Learning and Optimization Laboratory](https://www.epfl.ch/labs/mlo/), and [Natural Language Processing Lab](https://nlp.epfl.ch/).
- The open-source [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM) trainer was used for fine-tuning.
- [rombodawg](https://huggingface.co/rombodawg) curated the [LosslessMegaCodeTrainingV2_1m_Evol_Uncensored](https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored) dataset.
- [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.
- [Argilla](https://huggingface.co/argilla) curated and published the [argilla/databricks-dolly-15k-curated-multilingual](https://huggingface.co/datasets/argilla/databricks-dolly-15k-curated-multilingual) dataset.
- [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.
- [andreaskoepf](https://github.com/andreaskoepf/) prepared & orchestrated the training.

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.

## Ethical Considerations and Limitations

Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. 
For these reasons, as with all LLMs, the potential outputs of llama2-70b-oasst-sft-v10 cannot be predicted
in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
to user prompts. Therefore, before deploying any applications of llama2-70b-oasst-sft-v10, developers should
perform safety testing and tuning tailored to their specific applications of the model.

Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).

## Note regarding inference with TGI

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).
In contrast the model could be evaluated without problem using [vLLM](https://github.com/vllm-project/vllm).
The model also worked decently well when loaded with TGI on a single GPPU nf4 quantized via [TimDettmers/bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
Will will get it touch with the TGI authors to find out why sharded 16-bit inference doesn't work as expected.

## Configuration Details

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).


### Stage 1 Pretokenizer Configuration

Entries of the dataset with assistant replies shorter than 25 tokens were excluded from training.

```
oasst_pre10_min25:
  datasets:
    - megacode2:
        fraction: 0.5
        val_split: 0.01
        max_val_set: 1000
    - orca-chat:
        val_split: 0.01
        max_val_set: 1000
    - dolly15k_multilingual:
        val_split: 0.05
        max_val_set: 300
    - oa_leet10k:
        val_split: 0.05
        max_val_set: 250
  output_dir: "output/oasst_pre10_min25"
  filename_prefix: "oasst_pre10"
  min_assistant_tokens: 25
```

Stage 1 dataset statistics:
```
# Stats for output/oasst_pre10_min25_llama2

## Stats for 'Subset of InstructionDataset (megacode2)' (466364 samples (50.0%))
-----------------
  Accepted: 398223/466364 (85.4%)
  Accepted tokens: 167676873
  Skipped: 68141 (14.6%)
  Min tokens per sample: 36
  Max tokens per sample: 11810
  Avg tokens per sample: 421.063
-----------------

## Stats for 'Subset of OrcaChat (orca-chat)' (325616 samples (100.0%))
-----------------
  Accepted: 325616/325616 (100.0%)
  Accepted tokens: 178307574
  Skipped: 0 (0.0%)
  Min tokens per sample: 105
  Max tokens per sample: 10408
  Avg tokens per sample: 547.601
-----------------

## Stats for 'Subset of Dolly15kMultilingual' (57020 samples (100.0%))
-----------------
  Accepted: 47494/57020 (83.3%)
  Accepted tokens: 13883177
  Skipped: 9526 (16.7%)
  Min tokens per sample: 34
  Max tokens per sample: 9172
  Avg tokens per sample: 292.314
-----------------

## Stats for 'Subset of InstructionDataset (oa_leet10k)' (22236 samples (100.0%))
-----------------
  Accepted: 22236/22236 (100.0%)
  Accepted tokens: 15905296
  Skipped: 0 (0.0%)
  Min tokens per sample: 168
  Max tokens per sample: 10588
  Avg tokens per sample: 715.295
-----------------

## Stats for 'total' (871236 samples (100.0%))
-----------------
  Accepted: 793569/871236 (91.1%)
  Accepted tokens: 375772920
  Skipped: 77667 (8.9%)
  Min tokens per sample: 34
  Max tokens per sample: 11810
  Avg tokens per sample: 473.523
-----------------
```


### Stage 2 Pretokenizer Configuration

```
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"
        input_file_path: 2023-07-23_oasst_ready.tar.gz
        top_k: 1
        val_split: 0.05
  output_dir: "output/oasst_top1_2023-07-23"
  filename_prefix: "oasst_top1"
```

Stage 2 dataset statistics:

```
# Stats for output/oasst_top1_2023-07-23_llama2

## Stats for 'ListDataset' (11441 samples (100.0%))
-----------------
  Accepted: 11441/11441 (100.0%)
  Accepted tokens: 5315368
  Skipped: 0 (0.0%)
  Min tokens per sample: 20
  Max tokens per sample: 5407
  Avg tokens per sample: 464.58945896337735
-----------------

## Stats for 'total' (11441 samples (100.0%))
-----------------
  Accepted: 11441/11441 (100.0%)
  Accepted tokens: 5315368
  Skipped: 0 (0.0%)
  Min tokens per sample: 20
  Max tokens per sample: 5407
  Avg tokens per sample: 464.58945896337735
-----------------
```


### Megatron Fine-Tuning Arguments for Stage 1 (Instruction Tuning):
```
--tensor_model_parallel_size 8
--pipeline_model_parallel_size 4
--load ./checkpoints/llama2-70b-tp8-pp4
--save ./checkpoints/llama2-70b-tp8-pp4-oasst_pre10
--tensorboard_dir ./checkpoints/llama2-70b-tp8-pp4-oasst_pre10/logging
--data_path ./data/oasst_pre10_min25_llama2/oasst_sft10-train
--model_name llama2
--tokenizer_type SentencePieceTokenizer
--bf16
--global_batch_size 64
--micro_batch_size 2
--vocab_file=./llama2/Llama-2-7b/tokenizer.model
--use_rms_norm
--glu_activation swiglu
--no_tie_embed_logits
--vocab_extra_ids_list "\"<|im_start|>,<|im_end|>\""
--layernorm_epsilon 1e-5
--use_flash_attn
--no_bias_gelu_fusion
--seq_length 4096
--max_position_embeddings 4096
--log_interval 1
--save_interval 500
--eval_interval 50
--eval_iters 10
--hidden_dropout 0.0
--position_embedding_type rotary
--no_bias_dropout_fusion
--use_checkpoint_args
--train_iters 12000
--attention_dropout 0.0
--adam_beta1 0.9
--adam_beta2 0.95
--adam_eps 1e-12
--lr_decay_style cosine
--lr_warmup_iters 100
--lr 1e-5
--min_lr 1e-6
--weight_decay 0.000001
--sequence_parallel
--recompute_granularity selective
--log_timers_to_tensorboard
--rope_scaling_factor 1.0
--wandb_logger
```

### Megatron Fine-Tuning Arguments for Stage 2 (OASST Polishing, LIMA Dropout):
```
--tensor_model_parallel_size 8
--pipeline_model_parallel_size 4
--load ./checkpoints/llama2-70b-tp8-pp4-oasst_pre10
--save ./checkpoints/llama2-70b-tp8-pp4-oasst_sft10
--tensorboard_dir ./checkpoints/llama2-70b-tp8-pp4-oasst_sft10/logging
--data_path ./data/oasst_top1_2023-07-23_llama2/oasst_top1-train
--model_name llama2
--tokenizer_type SentencePieceTokenizer
--bf16
--global_batch_size 64
--micro_batch_size 2
--vocab_file=./llama2/Llama-2-7b/tokenizer.model
--use_rms_norm
--glu_activation swiglu
--no_tie_embed_logits
--vocab_extra_ids_list "\"<|im_start|>,<|im_end|>\""
--layernorm_epsilon 1e-5
--use_flash_attn
--no_bias_gelu_fusion
--seq_length 4096
--max_position_embeddings 4096
--log_interval 1
--save_interval 346
--eval_interval 50
--eval_iters 10
--hidden_dropout 0.25
--lima_dropout
--position_embedding_type rotary
--no_bias_dropout_fusion
--use_checkpoint_args
--train_iters 519
--attention_dropout 0.0
--adam_beta1 0.9
--adam_beta2 0.95
--adam_eps 1e-12
--lr_decay_style cosine
--lr_warmup_iters 100
--lr 1e-5
--min_lr 1e-6
--weight_decay 0.000001
--sequence_parallel
--recompute_granularity selective
--log_timers_to_tensorboard
--rope_scaling_factor 1.0
--finetune
--wandb_logger
```

<!-- original-model-card end -->