Update base_model formatting

68b5aec about 1 year ago

20.5 kB

	---
	language:
	- en
	license: llama2
	datasets:
	- OpenAssistant/oasst1
	- shahules786/orca-best
	model_name: CodeLlama 13B SFT v10
	base_model: OpenAssistant/codellama-13b-oasst-sft-v10
	inference: false
	model_creator: OpenAssistant
	model_type: llama
	prompt_template: '<\|im_start\|>system

	{system_message}<\|im_end\|>

	<\|im_start\|>user

	{prompt}<\|im_end\|>

	<\|im_start\|>assistant

	'
	quantized_by: TheBloke
	---

	<!-- header start -->
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	# CodeLlama 13B SFT v10 - GPTQ
	- Model creator: [OpenAssistant](https://huggingface.co/OpenAssistant)
	- Original model: [CodeLlama 13B SFT v10](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10)

	<!-- description start -->
	## Description

	This repo contains GPTQ model files for [OpenAssistant's CodeLlama 13B SFT v10](https://huggingface.co/OpenAssistant/codellama-13b-oasst-sft-v10).

	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.

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

	* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-AWQ)
	* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ)
	* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GGUF)
	* [OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenAssistant/codellama-13b-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 -->


	<!-- README_GPTQ.md-provided-files start -->
	## Provided files and GPTQ parameters

	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.

	All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.

	<details>
	<summary>Explanation of GPTQ parameters</summary>

	- Bits: The bit size of the quantised model.
	- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
	- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
	- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
	- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
	- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
	- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.

	</details>

	\| Branch \| Bits \| GS \| Act Order \| Damp % \| GPTQ Dataset \| Seq Len \| Size \| ExLlama \| Desc \|
	\| ------ \| ---- \| -- \| --------- \| ------ \| ------------ \| ------- \| ---- \| ------- \| ---- \|
	\| [main](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/main) \| 4 \| 128 \| No \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) \| 8192 \| 7.26 GB \| Yes \| 4-bit, without Act Order and group size 128g. \|
	\| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) \| 8192 \| 8.00 GB \| Yes \| 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. \|
	\| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) \| 8192 \| 7.51 GB \| Yes \| 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. \|
	\| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-4bit-128g-actorder_True) \| 4 \| 128 \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) \| 8192 \| 7.26 GB \| Yes \| 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. \|
	\| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) \| 8192 \| 13.36 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements. \|
	\| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) \| 8192 \| 13.65 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. \|

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

	<!-- README_GPTQ.md-download-from-branches start -->
	## How to download from branches

	- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ:main`
	- With Git, you can clone a branch with:
	```
	git clone --single-branch --branch main https://huggingface.co/TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ
	```
	- In Python Transformers code, the branch is the `revision` parameter; see below.
	<!-- README_GPTQ.md-download-from-branches end -->
	<!-- README_GPTQ.md-text-generation-webui start -->
	## 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're sure you know how to make a manual install.

	1. Click the Model tab.
	2. Under Download custom model or LoRA, enter `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ`.
	- To download from a specific branch, enter for example `TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ:main`
	- 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: `CodeLlama-13B-oasst-sft-v10-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 and should not set manual 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!
	<!-- README_GPTQ.md-text-generation-webui end -->

	<!-- README_GPTQ.md-use-from-python start -->
	## How to use this GPTQ model from Python code

	### Install the necessary packages

	Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

	```shell
	pip3 install transformers>=4.32.0 optimum>=1.12.0
	pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
	```

	If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:

	```shell
	pip3 uninstall -y auto-gptq
	git clone https://github.com/PanQiWei/AutoGPTQ
	cd AutoGPTQ
	pip3 install .
	```

	### For CodeLlama models only: you must use Transformers 4.33.0 or later.

	If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
	```shell
	pip3 uninstall -y transformers
	pip3 install git+https://github.com/huggingface/transformers.git
	```

	### You can then use the following code

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

	model_name_or_path = "TheBloke/CodeLlama-13B-oasst-sft-v10-GPTQ"
	# To use a different branch, change revision
	# For example: revision="main"
	model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
	device_map="auto",
	trust_remote_code=False,
	revision="main")

	tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

	prompt = "Tell me about AI"
	prompt_template=f'''<\|im_start\|>system
	{system_message}<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant

	'''

	print("\n\n*** Generate:")

	input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
	output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
	print(tokenizer.decode(output[0]))

	# Inference can also be done using transformers' pipeline

	print("*** Pipeline:")
	pipe = pipeline(
	"text-generation",
	model=model,
	tokenizer=tokenizer,
	max_new_tokens=512,
	do_sample=True,
	temperature=0.7,
	top_p=0.95,
	top_k=40,
	repetition_penalty=1.1
	)

	print(pipe(prompt_template)[0]['generated_text'])
	```
	<!-- README_GPTQ.md-use-from-python end -->

	<!-- README_GPTQ.md-compatibility start -->
	## Compatibility

	The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).

	[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

	[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
	<!-- README_GPTQ.md-compatibility end -->

	<!-- footer start -->
	<!-- 200823 -->
	## Discord

	For further support, and discussions on these models and AI in general, join us at:

	[TheBloke AI's Discord server](https://discord.gg/theblokeai)

	## Thanks, and how to contribute

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

	Thanks to Clay from [gpus.llm-utils.org](llm-utils)!

	I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

	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.

	* Patreon: https://patreon.com/TheBlokeAI
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	Special thanks to: Aemon Algiz.

	Patreon special mentions: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov


	Thank you to all my generous patrons and donaters!

	And thank you again to a16z for their generous grant.

	<!-- footer end -->

	# Original model card: OpenAssistant's CodeLlama 13B SFT v10

	# Open-Assistant CodeLlama 13B SFT v10

	This model is an Open-Assistant fine-tuning of Meta's CodeLlama 13B LLM.

	Note: Due to the new RoPE Theta value (1e6 instead of 1e4), for correct results you must load this model with `trust_remote_code=True` or use the latest main branch of Huggingface transformers (until version 4.33 is released).

	## Model Details

	- Finetuned from: [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) via [epfLLM/Megatron-LLM](https://github.com/epfLLM/Megatron-LLM)
	- Model type: Causal decoder-only transformer language model
	- Language: English
	- Weights & Biases training logs: 6123 steps, BS 64 [run56_oa_llamacode](https://wandb.ai/open-assistant/public-sft/runs/run56_oa_llamacode)
	- Demo: [Continuations for 250 random prompts (without system message)](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%2F2023-08-26_OpenAssistant_codellama-13b-oasst-sft-v10_sampling_noprefix2.json)
	- 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 CodeLLlama 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).
	- [shahules786](https://github.com/shahules786) de-duped and filtered the Dolphin and Megacode dataset with a clustering/controid approach and generated orca-best & bestofmegacode.
	- [andreaskoepf](https://github.com/andreaskoepf/) prepared & orchestrated the training.

	## 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 codellama-13b-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 codellama-13b-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/).

	## 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).


	### Pretokenizer Configuration


	```
	orca_megacode_oasst_best:
	datasets:
	- orca-chat:
	val_split: 0.01
	max_val_set: 1000
	- bestofmegacode:
	val_split: 0.01
	max_val_set: 1000
	- oasst_export:
	lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk"
	#hf_dataset_name: OpenAssistant/oasst1
	input_file_path: 2023-08-25_oasst_ready.jsonl.gz
	top_k: 1
	val_split: 0.025
	output_dir: "output/orca_megacode_oasst_best"
	filename_prefix: "orca_megacode_oasst_best"
	min_assistant_tokens: 1
	```