Initial GPTQ model commit

ca40211 over 1 year ago

15.6 kB

	---
	inference: false
	license: other
	model_creator: lmsys
	model_link: https://huggingface.co/lmsys/vicuna-13b-v1.5-16k
	model_name: Vicuna 13B v1.5 16K
	model_type: llama
	quantized_by: TheBloke
	---

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	# Vicuna 13B v1.5 16K - GPTQ
	- Model creator: [lmsys](https://huggingface.co/lmsys)
	- Original model: [Vicuna 13B v1.5 16K](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k)

	## Description

	This repo contains GPTQ model files for [lmsys's Vicuna 13B v1.5 16K](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k).

	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.

	## Repositories available

	* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ)
	* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GGML)
	* [lmsys's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k)

	## Prompt template: Vicuna

	```
	A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.

	USER: {prompt}
	ASSISTANT:
	```

	## 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 GPTQ files are made with AutoGPTQ.

	<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 issues with models that use Act Order plus Group Size.
	- 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/vicuna-13B-v1.5-16K-GPTQ/tree/main) \| 4 \| 128 \| No \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 8192 \| 7.26 GB \| Yes \| Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. \|
	\| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-4bit-32g-actorder_True) \| 4 \| 32 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 8192 \| 8.00 GB \| Yes \| 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](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-4bit-64g-actorder_True) \| 4 \| 64 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 8192 \| 7.51 GB \| Yes \| 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](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-4bit-128g-actorder_True) \| 4 \| 128 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 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. Poor AutoGPTQ CUDA speed. \|
	\| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-8bit--1g-actorder_True) \| 8 \| None \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 8192 \| 13.36 GB \| No \| 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. \|
	\| [gptq-8bit-64g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-8bit-64g-actorder_True) \| 8 \| 64 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 8192 \| 13.95 GB \| No \| 8-bit, with group size 64g and Act Order for even higher inference quality. Poor AutoGPTQ CUDA speed. \|
	\| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-8bit-32g-actorder_True) \| 8 \| 32 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 8192 \| 14.54 GB \| No \| 8-bit, with group size 32g and Act Order for maximum inference quality. Poor AutoGPTQ CUDA speed. \|
	\| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-GPTQ/tree/gptq-8bit-128g-actorder_True) \| 8 \| 128 \| Yes \| 0.1 \| [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) \| 8192 \| 13.65 GB \| No \| 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. \|

	## How to download from branches

	- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/vicuna-13B-v1.5-16K-GPTQ:gptq-4bit-32g-actorder_True`
	- With Git, you can clone a branch with:
	```
	git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/vicuna-13B-v1.5-16K-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/vicuna-13B-v1.5-16K-GPTQ`.
	- To download from a specific branch, enter for example `TheBloke/vicuna-13B-v1.5-16K-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: `vicuna-13B-v1.5-16K-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!

	## How to use this GPTQ model from Python code

	First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) 0.3.1 or later installed:

	```
	pip3 install auto-gptq
	```

	If you have problems installing AutoGPTQ, please build from source instead:
	```
	pip3 uninstall -y auto-gptq
	git clone https://github.com/PanQiWei/AutoGPTQ
	cd AutoGPTQ
	pip3 install .
	```

	Then try the following example code:

	```python
	from transformers import AutoTokenizer, pipeline, logging
	from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig

	model_name_or_path = "TheBloke/vicuna-13B-v1.5-16K-GPTQ"

	use_triton = False

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

	model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
	use_safetensors=True,
	trust_remote_code=False,
	device="cuda:0",
	use_triton=use_triton,
	quantize_config=None)

	"""
	# To download from a specific branch, use the revision parameter, as in this example:
	# Note that `revision` requires AutoGPTQ 0.3.1 or later!

	model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
	revision="gptq-4bit-32g-actorder_True",
	use_safetensors=True,
	trust_remote_code=False,
	device="cuda:0",
	quantize_config=None)
	"""

	prompt = "Tell me about AI"
	prompt_template=f'''A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.

	USER: {prompt}
	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, max_new_tokens=512)
	print(tokenizer.decode(output[0]))

	# Inference can also be done using transformers' pipeline

	# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
	logging.set_verbosity(logging.CRITICAL)

	print("*** Pipeline:")
	pipe = pipeline(
	"text-generation",
	model=model,
	tokenizer=tokenizer,
	max_new_tokens=512,
	temperature=0.7,
	top_p=0.95,
	repetition_penalty=1.15
	)

	print(pipe(prompt_template)[0]['generated_text'])
	```

	## Compatibility

	The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.

	ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.

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

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	# Original model card: lmsys's Vicuna 13B v1.5 16K


	# Vicuna Model Card

	## Model Details

	Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.

	- Developed by: [LMSYS](https://lmsys.org/)
	- Model type: An auto-regressive language model based on the transformer architecture
	- License: Llama 2 Community License Agreement
	- Finetuned from model: [Llama 2](https://arxiv.org/abs/2307.09288)

	### Model Sources

	- Repository: https://github.com/lm-sys/FastChat
	- Blog: https://lmsys.org/blog/2023-03-30-vicuna/
	- Paper: https://arxiv.org/abs/2306.05685
	- Demo: https://chat.lmsys.org/

	## Uses

	The primary use of Vicuna is research on large language models and chatbots.
	The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.

	## How to Get Started with the Model

	- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights
	- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api

	## Training Details

	Vicuna v1.5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling.
	The training data is around 125K conversations collected from ShareGPT.com. These conversations are packed into sequences that contain 16K tokens each.
	See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).

	## Evaluation

	![Evaluation Results](https://github.com/lm-sys/lm-sys.github.io/blob/main/public/images/webdata/vicuna_v1.5_eval.png?raw=true)

	Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).

	## Difference between different versions of Vicuna

	See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)