Yi-34B-Chat-AWQ / README.md

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	---
	base_model: 01-ai/Yi-34B-Chat
	inference: false
	license: other
	license_link: LICENSE
	license_name: yi-license
	model_creator: 01-ai
	model_name: Yi 34B Chat
	model_type: yi
	pipeline_tag: text-generation
	prompt_template: '<\|im_start\|>system

	{system_message}<\|im_end\|>

	<\|im_start\|>user

	{prompt}<\|im_end\|>

	<\|im_start\|>assistant

	'
	quantized_by: TheBloke
	widget:
	- example_title: Yi-34B-Chat
	output:
	text: ' Hello! How can I assist you today?'
	text: hi
	- example_title: Yi-34B
	output:
	text: " an eerie sense that something is just not right\u2026\nBetween the two\
	\ worlds lies The Forgotten Kingdom - home to creatures long since thought extinct\
	\ and ancient magic so strong it defies belief! Only here can you find what\
	\ has been lost for centuries: An Elixir Of Life which will restore youth and\
	\ vitality if only those who seek its power are brave enough to face up against\
	\ all manner of dangers lurking in this mysterious land! But beware; some say\
	\ there may even exist powerful entities beyond our comprehension whose intentions\
	\ towards humanity remain unclear at best ---- they might want nothing more\
	\ than destruction itself rather then anything else from their quest after immortality\
	\ (and maybe someone should tell them about modern medicine)? In any event though\
	\ \u2013 one thing remains true regardless : whether or not success comes easy\
	\ depends entirely upon how much effort we put into conquering whatever challenges\
	\ lie ahead along with having faith deep down inside ourselves too ;) So let\u2019\
	s get started now shall We?"
	text: There's a place where time stands still. A place of breath taking wonder,
	but also
	---
	<!-- markdownlint-disable MD041 -->

	<!-- header start -->
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	<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
	<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
	<!-- header end -->

	# Yi 34B Chat - AWQ
	- Model creator: [01-ai](https://huggingface.co/01-ai)
	- Original model: [Yi 34B Chat](https://huggingface.co/01-ai/Yi-34B-Chat)

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

	This repo contains AWQ model files for [01-ai's Yi 34B Chat](https://huggingface.co/01-ai/Yi-34B-Chat).

	These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).


	### About AWQ

	AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

	It is supported by:

	- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
	- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
	- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
	- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
	- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

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

	* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Yi-34B-Chat-AWQ)
	* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Yi-34B-Chat-GPTQ)
	* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Yi-34B-Chat-GGUF)
	* [01-ai's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/01-ai/Yi-34B-Chat)
	<!-- 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_AWQ.md-provided-files start -->
	## Provided files, and AWQ parameters

	I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.

	Models are released as sharded safetensors files.

	\| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \|
	\| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \|
	\| [main](https://huggingface.co/TheBloke/Yi-34B-Chat-AWQ/tree/main) \| 4 \| 128 \| [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 19.23 GB

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

	<!-- README_AWQ.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/Yi-34B-Chat-AWQ`.
	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: `Yi-34B-Chat-AWQ`
	7. Select Loader: AutoAWQ.
	8. Click Load, and the model will load and is now ready for use.
	9. 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.
	10. Once you're ready, click the Text Generation tab and enter a prompt to get started!
	<!-- README_AWQ.md-text-generation-webui end -->

	<!-- README_AWQ.md-use-from-vllm start -->
	## Multi-user inference server: vLLM

	Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).

	- Please ensure you are using vLLM version 0.2 or later.
	- When using vLLM as a server, pass the `--quantization awq` parameter.

	For example:

	```shell
	python3 -m vllm.entrypoints.api_server --model TheBloke/Yi-34B-Chat-AWQ --quantization awq --dtype auto
	```

	- When using vLLM from Python code, again set `quantization=awq`.

	For example:

	```python
	from vllm import LLM, SamplingParams

	prompts = [
	"Tell me about AI",
	"Write a story about llamas",
	"What is 291 - 150?",
	"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
	]
	prompt_template=f'''<\|im_start\|>system
	{system_message}<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant
	'''

	prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]

	sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

	llm = LLM(model="TheBloke/Yi-34B-Chat-AWQ", quantization="awq", dtype="auto")

	outputs = llm.generate(prompts, sampling_params)

	# Print the outputs.
	for output in outputs:
	prompt = output.prompt
	generated_text = output.outputs[0].text
	print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
	```
	<!-- README_AWQ.md-use-from-vllm start -->

	<!-- README_AWQ.md-use-from-tgi start -->
	## Multi-user inference server: Hugging Face Text Generation Inference (TGI)

	Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`

	Example Docker parameters:

	```shell
	--model-id TheBloke/Yi-34B-Chat-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
	```

	Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):

	```shell
	pip3 install huggingface-hub
	```

	```python
	from huggingface_hub import InferenceClient

	endpoint_url = "https://your-endpoint-url-here"

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

	client = InferenceClient(endpoint_url)
	response = client.text_generation(prompt,
	max_new_tokens=128,
	do_sample=True,
	temperature=0.7,
	top_p=0.95,
	top_k=40,
	repetition_penalty=1.1)

	print(f"Model output: ", response)
	```
	<!-- README_AWQ.md-use-from-tgi end -->

	<!-- README_AWQ.md-use-from-python start -->
	## Inference from Python code using Transformers

	### Install the necessary packages

	- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
	- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.

	```shell
	pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
	```

	Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.

	If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:

	```shell
	pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
	```

	If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:

	```shell
	pip3 uninstall -y autoawq
	git clone https://github.com/casper-hansen/AutoAWQ
	cd AutoAWQ
	pip3 install .
	```

	### Transformers example code (requires Transformers 4.35.0 and later)

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

	model_name_or_path = "TheBloke/Yi-34B-Chat-AWQ"

	tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
	model = AutoModelForCausalLM.from_pretrained(
	model_name_or_path,
	low_cpu_mem_usage=True,
	device_map="cuda:0"
	)

	# Using the text streamer to stream output one token at a time
	streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=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
	'''

	# Convert prompt to tokens
	tokens = tokenizer(
	prompt_template,
	return_tensors='pt'
	).input_ids.cuda()

	generation_params = {
	"do_sample": True,
	"temperature": 0.7,
	"top_p": 0.95,
	"top_k": 40,
	"max_new_tokens": 512,
	"repetition_penalty": 1.1
	}

	# Generate streamed output, visible one token at a time
	generation_output = model.generate(
	tokens,
	streamer=streamer,
	**generation_params
	)

	# Generation without a streamer, which will include the prompt in the output
	generation_output = model.generate(
	tokens,
	**generation_params
	)

	# Get the tokens from the output, decode them, print them
	token_output = generation_output[0]
	text_output = tokenizer.decode(token_output)
	print("model.generate output: ", text_output)

	# Inference is also possible via Transformers' pipeline
	from transformers import pipeline

	pipe = pipeline(
	"text-generation",
	model=model,
	tokenizer=tokenizer,
	**generation_params
	)

	pipe_output = pipe(prompt_template)[0]['generated_text']
	print("pipeline output: ", pipe_output)

	```
	<!-- README_AWQ.md-use-from-python end -->

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

	The files provided are tested to work with:

	- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
	- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
	- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
	- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
	- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.

	<!-- README_AWQ.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
	* Ko-Fi: https://ko-fi.com/TheBlokeAI

	Special thanks to: Aemon Algiz.

	Patreon special mentions: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius


	Thank you to all my generous patrons and donaters!

	And thank you again to a16z for their generous grant.

	<!-- footer end -->

	# Original model card: 01-ai's Yi 34B Chat



	<div align="center">

	<p align="center">
	<img width="200px" src="https://github.com/01-ai/Yi/raw/main/assets/img/Yi.svg?sanitize=true">
	</p>

	<div style="display: inline-block;">
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	</div>

	</div>

	## Introduction

	The Yi series models are large language models trained from scratch by
	developers at [01.AI](https://01.ai/).

	## News

	<details open>
	<summary>🎯 <b>2023/11/23</b>: The chat models are open to public.</summary>

	This release contains two chat models based on previous released base models, two 8-bits models quntinized by GPTQ, two 4-bits models quantinized by AWQ.

	- `Yi-34B-Chat`
	- `Yi-34B-Chat-4bits`
	- `Yi-34B-Chat-8bits`
	- `Yi-6B-Chat`
	- `Yi-6B-Chat-4bits`
	- `Yi-6B-Chat-8bits`

	You can try some of them interactively at:

	- [HuggingFace](https://huggingface.co/spaces/01-ai/Yi-34B-Chat)
	- [Replicate](https://replicate.com/01-ai)
	</details>

	<details open>
	<summary>🔔 <b>2023/11/23</b>: The Yi Series Models Community License Agreement is updated to v2.1.</summary>
	</details>

	<details>
	<summary>🔥 <b>2023/11/08</b>: Invited test of Yi-34B chat model.</summary>

	Application form:

	- [English](https://cn.mikecrm.com/l91ODJf)
	- [Chinese](https://cn.mikecrm.com/gnEZjiQ)

	</details>

	<details>
	<summary>🎯 <b>2023/11/05</b>: The base model of <code>Yi-6B-200K</code> and <code>Yi-34B-200K</code>.</summary>

	This release contains two base models with the same parameter sizes of previous
	release, except that the context window is extended to 200K.

	</details>

	<details>
	<summary>🎯 <b>2023/11/02</b>: The base model of <code>Yi-6B</code> and <code>Yi-34B</code>.</summary>

	The first public release contains two bilingual (English/Chinese) base models
	with the parameter sizes of 6B and 34B. Both of them are trained with 4K
	sequence length and can be extended to 32K during inference time.

	</details>

	## Model Performance

	### Base Model Performance

	\| Model \| MMLU \| CMMLU \| C-Eval \| GAOKAO \| BBH \| Common-sense Reasoning \| Reading Comprehension \| Math & Code \|
	\| :------------ \| :------: \| :------: \| :------: \| :------: \| :------: \| :--------------------: \| :-------------------: \| :---------: \|
	\| \| 5-shot \| 5-shot \| 5-shot \| 0-shot \| 3-shot@1 \| - \| - \| - \|
	\| LLaMA2-34B \| 62.6 \| - \| - \| - \| 44.1 \| 69.9 \| 68.0 \| 26.0 \|
	\| LLaMA2-70B \| 68.9 \| 53.3 \| - \| 49.8 \| 51.2 \| 71.9 \| 69.4 \| 36.8 \|
	\| Baichuan2-13B \| 59.2 \| 62.0 \| 58.1 \| 54.3 \| 48.8 \| 64.3 \| 62.4 \| 23.0 \|
	\| Qwen-14B \| 66.3 \| 71.0 \| 72.1 \| 62.5 \| 53.4 \| 73.3 \| 72.5 \| 39.8 \|
	\| Skywork-13B \| 62.1 \| 61.8 \| 60.6 \| 68.1 \| 41.7 \| 72.4 \| 61.4 \| 24.9 \|
	\| InternLM-20B \| 62.1 \| 59.0 \| 58.8 \| 45.5 \| 52.5 \| 78.3 \| - \| 30.4 \|
	\| Aquila-34B \| 67.8 \| 71.4 \| 63.1 \| - \| - \| - \| - \| - \|
	\| Falcon-180B \| 70.4 \| 58.0 \| 57.8 \| 59.0 \| 54.0 \| 77.3 \| 68.8 \| 34.0 \|
	\| Yi-6B \| 63.2 \| 75.5 \| 72.0 \| 72.2 \| 42.8 \| 72.3 \| 68.7 \| 19.8 \|
	\| Yi-6B-200K \| 64.0 \| 75.3 \| 73.5 \| 73.9 \| 42.0 \| 72.0 \| 69.1 \| 19.0 \|
	\| Yi-34B \| 76.3 \| 83.7 \| 81.4 \| 82.8 \| 54.3 \| 80.1 \| 76.4 \| 37.1 \|
	\| Yi-34B-200K \| 76.1 \| 83.6 \| 81.9 \| 83.4 \| 52.7 \| 79.7 \| 76.6 \| 36.3 \|

	While benchmarking open-source models, we have observed a disparity between the
	results generated by our pipeline and those reported in public sources (e.g.
	OpenCompass). Upon conducting a more in-depth investigation of this difference,
	we have discovered that various models may employ different prompts,
	post-processing strategies, and sampling techniques, potentially resulting in
	significant variations in the outcomes. Our prompt and post-processing strategy
	remains consistent with the original benchmark, and greedy decoding is employed
	during evaluation without any post-processing for the generated content. For
	scores that were not reported by the original authors (including scores reported
	with different settings), we try to get results with our pipeline.

	To evaluate the model's capability extensively, we adopted the methodology
	outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande,
	ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ
	were incorporated to evaluate reading comprehension. CSQA was exclusively tested
	using a 7-shot setup, while all other tests were conducted with a 0-shot
	configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1),
	HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code". Due
	to technical constraints, we did not test Falcon-180 on QuAC and OBQA; the score
	is derived by averaging the scores on the remaining tasks. Since the scores for
	these two tasks are generally lower than the average, we believe that
	Falcon-180B's performance was not underestimated.

	### Chat Model Performance

	\| Model \| MMLU \| MMLU \| CMMLU \| CMMLU \| C-Eval(val)<sup></sup> \| C-Eval(val)<sup></sup> \| Truthful QA \| BBH \| BBH \| GSM8k \| GSM8k \|
	\| ----------------------- \| --------- \| --------- \| --------- \| --------- \| ----------------------- \| ----------------------- \| ----------- \| --------- \| --------- \| --------- \| --------- \|
	\| \| 0-shot \| 5-shot \| 0-shot \| 5-shot \| 0-shot \| 5-shot \| 0-shot \| 0-shot \| 3-shot \| 0-shot \| 4-shot \|
	\| LLaMA2-13B-Chat \| 50.88 \| 47.33 \| 27.47 \| 35.08 \| 27.93 \| 35.88 \| 36.84 \| 32.90 \| 58.22 \| 36.85 \| 2.73 \|
	\| LLaMA2-70B-Chat \| 59.42 \| 59.86 \| 36.10 \| 40.99 \| 34.99 \| 41.31 \| 53.95 \| 42.36 \| 58.53 \| 47.08 \| 58.68 \|
	\| Baichuan2-13B-Chat \| 55.09 \| 50.14 \| 58.64 \| 59.47 \| 56.02 \| 54.75 \| 48.98 \| 38.81 \| 47.15 \| 45.72 \| 23.28 \|
	\| Qwen-14B-Chat \| 63.99 \| 64.98 \| 67.73 \| 70.57 \| 66.12 \| 70.06 \| 52.49 \| 49.65 \| 54.98 \| 59.51 \| 61.18 \|
	\| InternLM-Chat-20B \| 55.55 \| 57.42 \| 53.55 \| 53.75 \| 51.19 \| 53.57 \| 51.75 \| 42.41 \| 36.68 \| 15.69 \| 43.44 \|
	\| AquilaChat2-34B v1.2 \| 65.15 \| 66.70 \| 67.51 \| 70.02 \| 82.99 \| 89.38 \| 64.33 \| 20.12 \| 34.28 \| 11.52 \| 48.45 \|
	\| Yi-6B-Chat \| 58.24 \| 60.99 \| 69.44 \| 74.71 \| 68.80 \| 74.22 \| 50.58 \| 39.70 \| 47.15 \| 38.44 \| 44.88 \|
	\| Yi-6B-Chat-8bits(GPTQ) \| 58.29 \| 60.96 \| 69.21 \| 74.69 \| 69.17 \| 73.85 \| 49.85 \| 40.35 \| 47.26 \| 39.42 \| 44.88 \|
	\| Yi-6B-Chat-4bits(AWQ) \| 56.78 \| 59.89 \| 67.70 \| 73.29 \| 67.53 \| 72.29 \| 50.29 \| 37.74 \| 43.62 \| 35.71 \| 38.36 \|
	\| Yi-34B-Chat \| 67.62 \| 73.46 \| 79.11 \| 81.34 \| 77.04 \| 78.53 \| 62.43 \| 51.41 \| 71.74 \| 71.65 \| 75.97 \|
	\| Yi-34B-Chat-8bits(GPTQ) \| 66.24 \| 73.69 \| 79.05 \| 81.23 \| 76.82 \| 78.97 \| 61.84 \| 52.08 \| 70.97 \| 70.74 \| 75.74 \|
	\| Yi-34B-Chat-4bits(AWQ) \| 65.77 \| 72.42 \| 78.21 \| 80.50 \| 75.71 \| 77.27 \| 61.84 \| 48.30 \| 69.39 \| 70.51 \| 74.00 \|

	We evaluated various benchmarks using both zero-shot and few-shot methods, except for TruthfulQA. Generally, the zero-shot approach is more common in chat models. Our evaluation strategy involves generating responses while following instructions explicitly or implicitly (such as using few-shot examples). We then isolate relevant answers from the generated text. Some models are not well-suited to produce output in the specific format required by instructions in few datasets, which leads to suboptimal results.

	<strong>*</strong>: C-Eval results are evaluated on the validation datasets

	### Quantized Chat Model Performance

	We also provide both 4-bit (AWQ) and 8-bit (GPTQ) quantized Yi chat models. Evaluation results on various benchmarks have shown that the quantized models have negligible losses. Additionally, they reduce the memory footprint size. After testing different configurations of prompts and generation lengths, we highly recommend following the guidelines in the memory footprint table below when selecting a device to run our models.

	\| \| batch=1 \| batch=4 \| batch=16 \| batch=32 \|
	\| ----------------------- \| ------- \| ------- \| -------- \| -------- \|
	\| Yi-34B-Chat \| 65GiB \| 68GiB \| 76GiB \| >80GiB \|
	\| Yi-34B-Chat-8bits(GPTQ) \| 35GiB \| 37GiB \| 46GiB \| 58GiB \|
	\| Yi-34B-Chat-4bits(AWQ) \| 19GiB \| 20GiB \| 30GiB \| 40GiB \|
	\| Yi-6B-Chat \| 12GiB \| 13GiB \| 15GiB \| 18GiB \|
	\| Yi-6B-Chat-8bits(GPTQ) \| 7GiB \| 8GiB \| 10GiB \| 14GiB \|
	\| Yi-6B-Chat-4bits(AWQ) \| 4GiB \| 5GiB \| 7GiB \| 10GiB \|

	Note: All the numbers in the table represent the minimum recommended memory for running models of the corresponding size.

	### Limitations of Chat Model

	The released chat model has undergone exclusive training using Supervised Fine-Tuning (SFT). Compared to other standard chat models, our model produces more diverse responses, making it suitable for various downstream tasks, such as creative scenarios. Furthermore, this diversity is expected to enhance the likelihood of generating higher quality responses, which will be advantageous for subsequent Reinforcement Learning (RL) training.

	However, this higher diversity might amplify certain existing issues, including:

	- Hallucination: This refers to the model generating factually incorrect or nonsensical information. With the model's responses being more varied, there's a higher chance of hallucination that are not based on accurate data or logical reasoning.
	- Non-determinism in re-generation: When attempting to regenerate or sample responses, inconsistencies in the outcomes may occur. The increased diversity can lead to varying results even under similar input conditions.
	- Cumulative Error: This occurs when errors in the model's responses compound over time. As the model generates more diverse responses, the likelihood of small inaccuracies building up into larger errors increases, especially in complex tasks like extended reasoning, mathematical problem-solving, etc.

	To achieve more coherent and consistent responses, it is advisable to adjust generation configuration parameters such as`temperature`,`top_p`, or`top_k`. These adjustments can help in the balance between creativity and coherence in the model's outputs.



	## Usage

	Feel free to [create an issue](https://github.com/01-ai/Yi/issues/new) if you
	encounter any problem when using the Yi series models.

	### 1. Prepare development environment

	#### 1.1 Docker
	The best approach to try the Yi series models is through Docker with GPUs. We
	provide the following docker images to help you get started.

	- `registry.lingyiwanwu.com/ci/01-ai/yi:latest`
	- `ghcr.io/01-ai/yi:latest`

	Note that the `latest` tag always points to the latest code in the `main`
	branch. To test a stable version, please replace it with a specific
	[tag](https://github.com/01-ai/Yi/tags).

	#### 1.2 Local development environment
	We use [`conda-lock`](https://github.com/conda/conda-lock) to generate fully reproducible lock files for conda environments. You can refer to [conda-lock.yml](./conda-lock.yml) for the exact versions of the dependencies. Additionally, we utilize [`micromamba`](https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html) for installing these dependencies.

	To install the dependencies, please follow these steps:
	1. Install `micromamba` by following the instructions available [here](https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html).
	2. Execute `micromamba install -y -n yi -f conda-lock.yml` to create a conda environment named `yi` and install the necessary dependencies.

	### 2. Download the model (optional)

	By default, the model weights and tokenizer will be downloaded from
	[HuggingFace](https://huggingface.co/01-ai) automatically in the next step. You
	can also download them manually from the following places:

	- [ModelScope](https://www.modelscope.cn/organization/01ai/)
	- [WiseModel](https://wisemodel.cn/organization/01.AI)

	### 3. Examples

	#### 3.1 Use the chat model

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_path = '01-ai/Yi-34b-Chat'

	tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)

	# Since transformers 4.35.0, the GPT-Q/AWQ model can be loaded using AutoModelForCausalLM.
	model = AutoModelForCausalLM.from_pretrained(
	model_path,
	device_map="auto",
	torch_dtype='auto'
	).eval()

	# Prompt content: "hi"
	messages = [
	{"role": "user", "content": "hi"}
	]

	input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
	output_ids = model.generate(input_ids.to('cuda'))
	response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

	# Model response: "Hello! How can I assist you today?"
	print(response)
	```

	#### 3.2 Use the base model

	```bash
	python demo/text_generation.py
	```

	To reuse the downloaded models in the previous step, you can provide the extra
	`--model` argument:

	```bash
	python demo/text_generation.py --model /path/to/model
	```

	Or if you'd like to get your hands dirty:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34B", device_map="auto", torch_dtype="auto", trust_remote_code=True)
	tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-34B", trust_remote_code=True)
	inputs = tokenizer("There's a place where time stands still. A place of breath taking wonder, but also", return_tensors="pt")
	max_length = 256

	outputs = model.generate(
	inputs.input_ids.cuda(),
	max_length=max_length,
	eos_token_id=tokenizer.eos_token_id,
	do_sample=True,
	repetition_penalty=1.3,
	no_repeat_ngram_size=5,
	temperature=0.7,
	top_k=40,
	top_p=0.8,
	)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```

	<details>

	<summary>Output</summary>

	Prompt: There's a place where time stands still. A place of breath taking wonder, but also

	Generation: There's a place where time stands still. A place of breath taking wonder, but also of great danger. A place where the very air you breathe could kill you. A place where the only way to survive is to be prepared.
	The place is called the Arctic.
	The Arctic is a vast, frozen wilderness. It is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end.
	The Arctic is also a place of great beauty. The ice and snow are a pristine white. The sky is a deep blue. The sunsets are spectacular.
	But the Arctic is also a place of great danger. The ice can be treacherous. The winds can be deadly. The sun can be blinding.
	The Arctic is a place where the only way to survive is to be prepared.
	The Arctic is a place of extremes. The temperatures can drop to -40 degrees Celsius. The winds can reach speeds of 100 kilometers per hour. The sun can shine for 24 hours a day, or not at all for weeks on end.
	The Arctic is a place of great beauty. The ice and snow are a

	</details>

	For more advanced usage, please refer to the
	[doc](https://github.com/01-ai/Yi/tree/main/demo).

	#### 3.3 Finetuning from the base model:

	```bash
	bash finetune/scripts/run_sft_Yi_6b.sh
	```

	Once finished, you can compare the finetuned model and the base model with the following command:

	```bash
	bash finetune/scripts/run_eval.sh
	```

	For more advanced usage like fine-tuning based on your custom data, please refer
	the [doc](https://github.com/01-ai/Yi/tree/main/finetune).

	#### 3.4 Quantization

	##### GPT-Q
	```bash
	python quantization/gptq/quant_autogptq.py \
	--model /base_model \
	--output_dir /quantized_model \
	--trust_remote_code
	```

	Once finished, you can then evaluate the resulting model as follows:

	```bash
	python quantization/gptq/eval_quantized_model.py \
	--model /quantized_model \
	--trust_remote_code
	```

	For a more detailed explanation, please read the [doc](https://github.com/01-ai/Yi/tree/main/quantization/gptq)

	##### AWQ
	```bash
	python quantization/awq/quant_autoawq.py \
	--model /base_model \
	--output_dir /quantized_model \
	--trust_remote_code
	```

	Once finished, you can then evaluate the resulted model as follows:

	```bash
	python quantization/awq/eval_quantized_model.py \
	--model /quantized_model \
	--trust_remote_code
	```

	For more detailed explanation, please read the [doc](https://github.com/01-ai/Yi/tree/main/quantization/awq)

	## Ecosystem

	🤗 You are encouraged to create a PR and share your awesome work built on top of
	the Yi series models.

	- Serving
	- [ScaleLLM](https://github.com/vectorch-ai/ScaleLLM#supported-models): Efficiently run Yi models locally.
	- Quantization
	- [TheBloke/Yi-34B-GGUF](https://huggingface.co/TheBloke/Yi-34B-GGUF)
	- [TheBloke/Yi-34B-GPTQ](https://huggingface.co/TheBloke/Yi-34B-GPTQ)
	- Finetuning
	- [NousResearch/Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B)

	## FAQ

	1. What dataset was this trained with?

	The dataset we use contains Chinese & English only. We used approximately 3T
	tokens. The detailed number and its construction will be described in the
	upcoming technical report.

	## Disclaimer

	We use data compliance checking algorithms during the training process, to
	ensure the compliance of the trained model to the best of our ability. Due to
	complex data and the diversity of language model usage scenarios, we cannot
	guarantee that the model will generate correct, and reasonable output in all
	scenarios. Please be aware that there is still a risk of the model producing
	problematic outputs. We will not be responsible for any risks and issues
	resulting from misuse, misguidance, illegal usage, and related misinformation,
	as well as any associated data security concerns.

	## License

	The source code in this repo is licensed under the [Apache 2.0
	license](https://github.com/01-ai/Yi/blob/main/LICENSE). The Yi series models
	are fully open for academic research and free commercial usage with permission
	via applications. All usage must adhere to the [Model License
	Agreement 2.0](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt).
	To apply for the official commercial license, please contact us
	([yi@01.ai](mailto:yi@01.ai)).