update README.md

README.md

@@ -12,71 +12,39 @@ tags:
 - llama
 - llama-2
 - gptq
+- orca
 ---
 
-# Meta's Llama 2 13B GPTQ
 
-These files are GPTQ model files for [Meta's Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b-hf).
+# Llama-2-13B-GPTQ-Orca
 
-Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
+This model is a fine-tuned version of [TheBloke/Llama-2-13B-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ) on Orca dataset [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca).
 
 
-## Repositories available
-
-* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-13B-GGML)
-* [Original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Llama-2-13B-fp16)
-
-## Prompt template: None
+## Prompt template:
 
 ```
 ### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:
 ```
 
-## Provided files
-
-Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
-
-Each separate quant is in a different branch.  See below for instructions on fetching from different branches.
-
-| Branch | Bits | Group Size | Act Order (desc_act) | File Size | ExLlama Compatible? | Made With | Description |
-| ------ | ---- | ---------- | -------------------- | --------- | ------------------- | --------- | ----------- |
-| main | 4 | 128 | False | 7.26 GB | True | AutoGPTQ | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
-| gptq-4bit-32g-actorder_True | 4 | 32 | True | 8.00 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 32g gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
-| gptq-4bit-64g-actorder_True | 4 | 64 | True | 7.51 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 64g uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
-| gptq-4bit-128g-actorder_True | 4 | 128 | True | 7.26 GB | True | AutoGPTQ | 4-bit, with Act Order and group size. 128g uses even less VRAM, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
-| gptq-8bit-128g-actorder_True | 8 | 128 | True | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. |
-| gptq-8bit-64g-actorder_True | 8 | 64 | True | 13.95 GB | False | AutoGPTQ | 8-bit, with group size 64g and Act Order for maximum inference quality. Poor AutoGPTQ CUDA speed. |
-| gptq-8bit-128g-actorder_False | 8 | 128 | False | 13.65 GB | False | AutoGPTQ | 8-bit, with group size 128g for higher inference quality and without Act Order to improve AutoGPTQ speed. |
-| gptq-8bit--1g-actorder_True | 8 | None | True | 13.36 GB | False | AutoGPTQ | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
-
-## How to download from branches
-
-- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Llama-2-13B-GPTQ:gptq-4bit-32g-actorder_True`
-- With Git, you can clone a branch with:
-```
-git clone --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Llama-2-13B-GPTQ`
-```
-- In Python Transformers code, the branch is the `revision` parameter; see below.
-
-## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
-
-Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
-
-It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.
-
-1. Click the **Model tab**.
-2. Under **Download custom model or LoRA**, enter `TheBloke/Llama-2-13B-GPTQ`.
-  - To download from a specific branch, enter for example `TheBloke/Llama-2-13B-GPTQ:gptq-4bit-32g-actorder_True`
-  - see Provided Files above for the list of branches for each option.
-3. Click **Download**.
-4. The model will start downloading. Once it's finished it will say "Done"
-5. In the top left, click the refresh icon next to **Model**.
-6. In the **Model** dropdown, choose the model you just downloaded: `Llama-2-13B-GPTQ`
-7. The model will automatically load, and is now ready for use!
-8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
-  * Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
-9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
+The model was trained with the following 16 system messages used to generate the training examples (see [ORCA paper](https://arxiv.org/abs/2306.02707)):
+
+1. \<empty system message\>
+2. You are an AI assistant. Provide a detailed answer so user don’t need to search outside to understand the answer.
+3. You are an AI assistant. You will be given a task. You must generate a detailed and long answer.
+4. You are a helpful assistant, who always provide explanation. Think like you are answering to a five year old.
+5. You are an AI assistant that follows instruction extremely well. Help as much as you can.
+6. You are an AI assistant that helps people find information. Provide a detailed answer so user don’t need to search outside to understand the answer.
+7. You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
+8. You should describe the task and explain your answer. While answering a multiple choice question, first output the correct answer(s). Then explain why other answers are wrong. Think like you are answering to a five year old.
+9. Explain how you used the definition to come up with the answer.
+10. You are an AI assistant. You should describe the task and explain your answer. While answering a multiple choice question, first output the correct answer(s). Then explain why other answers are wrong. You might need to use additional knowledge to answer the question.
+11. You are an AI assistant that helps people find information. User will you give you a question. Your task is to answer as faithfully as you can. While answering think step-by- step and justify your answer.
+12. User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer.
+13. You are a teacher. Given a task, you explain in simple steps what the task is asking, any guidelines it provides and how to use those guidelines to find the answer.
+14. You are an AI assistant, who knows every language and how to translate one language to another. Given a task, you explain in simple steps what the task is asking, any guidelines that it provides. You solve the task and show how you used the guidelines to solve the task.
+15. Given a definition of a task and a sample input, break the definition into small parts. Each of those parts will have some instruction. Explain their meaning by showing an example that meets the criteria in the instruction. Use the following format: Part \#: a key part of the definition. Usage: Sample response that meets the criteria from the key part. Explain why you think it meets the criteria.
+16. You are an AI assistant that helps people find information.
 
 ## How to use this GPTQ model from Python code
 
@@ -84,16 +52,10 @@ First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) instal
 
 `GITHUB_ACTIONS=true pip install auto-gptq`
 
-Then try the following example code:
+In order to use this, you need to download the base model from [TheBloke/Llama-2-13B-GPTQ](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ) and then load the adpter from this repo. Then try the following example code:
 
 ```python
 from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
-from threading import Thread
-import gc
-import traceback
-import asyncio
-import json
-from websockets.server import serve
 from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig, get_gptq_peft_model
 
 
@@ -128,119 +90,6 @@ The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLa
 
 ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
 
+# Developers
 
-# Original model card: Meta's Llama 2 13B
-
-# **Llama 2**
-Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
-
-## Model Details
-*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
-
-Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
-
-**Model Developers** Meta
-
-**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
-
-**Input** Models input text only.
-
-**Output** Models generate text only.
-
-**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
-
-
-||Training Data|Params|Content Length|GQA|Tokens|LR|
-|---|---|---|---|---|---|---|
-|Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
-|Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10<sup>-4</sup>|
-|Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10<sup>-4</sup>|
-
-*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models -  70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
-
-**Model Dates** Llama 2 was trained between January 2023 and July 2023.
-
-**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
-
-**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
-
-## Intended Use
-**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
-
-To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
-
-**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
-
-## Hardware and Software
-**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
-
-**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
-
-||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
-|---|---|---|---|
-|Llama 2 7B|184320|400|31.22|
-|Llama 2 13B|368640|400|62.44|
-|Llama 2 70B|1720320|400|291.42|
-|Total|3311616||539.00|
-
-**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
-
-## Training Data
-**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
-
-**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
-
-## Evaluation Results
-
-In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
-
-|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
-|---|---|---|---|---|---|---|---|---|---|
-|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
-|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
-|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
-|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
-|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
-|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
-|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
-
-**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
-
-|||TruthfulQA|Toxigen|
-|---|---|---|---|
-|Llama 1|7B|27.42|23.00|
-|Llama 1|13B|41.74|23.08|
-|Llama 1|33B|44.19|22.57|
-|Llama 1|65B|48.71|21.77|
-|Llama 2|7B|33.29|**21.25**|
-|Llama 2|13B|41.86|26.10|
-|Llama 2|70B|**50.18**|24.60|
-
-**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
-
-
-|||TruthfulQA|Toxigen|
-|---|---|---|---|
-|Llama-2-Chat|7B|57.04|**0.00**|
-|Llama-2-Chat|13B|62.18|**0.00**|
-|Llama-2-Chat|70B|**64.14**|0.01|
-
-**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
-
-## Ethical Considerations and Limitations
-Llama 2 is a new technology that carries risks with use. 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, Llama 2’s potential outputs 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 Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
-
-Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
-
-## Reporting Issues
-Please report any software “bug,” or other problems with the models through one of the following means:
-- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
-- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
-- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
-
-## Llama Model Index
-|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
-|---|---|---|---|---|
-|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
-|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
-|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
+- [tridungduong16](https://github.com/tridungduong16)