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	---
	license: llama2
	library_name: transformers
	tags:
	- code
	metrics:
	- code_eval
	base_model: WizardLM/WizardCoder-Python-34B-V1.0
	inference: false
	model_creator: WizardLM
	model_type: llama
	prompt_template: 'Below is an instruction that describes a task. Write a response
	that appropriately completes the request.


	### Instruction:

	{prompt}


	### Response:

	'
	quantized_by: TheBloke
	model-index:
	- name: WizardCoder-Python-34B-V1.0
	results:
	- task:
	type: text-generation
	dataset:
	name: HumanEval
	type: openai_humaneval
	metrics:
	- type: pass@1
	value: 0.732
	name: pass@1
	verified: false
	---

	<!-- header start -->
	<!-- 200823 -->
	<div style="width: auto; margin-left: auto; margin-right: auto">
	<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
	</div>
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	<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
	</div>
	<div style="display: flex; flex-direction: column; align-items: flex-end;">
	<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
	</div>
	</div>
	<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 -->

	# WizardCoder Python 34B V1.0 - AWQ
	- Model creator: [WizardLM](https://huggingface.co/WizardLM)
	- Original model: [WizardCoder Python 34B V1.0](https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0)

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

	This repo contains AWQ model files for [WizardLM's WizardCoder Python 34B V1.0](https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0).


	### 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.

	It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB.
	<!-- description end -->
	<!-- repositories-available start -->
	## Repositories available

	* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WizardCoder-Python-34B-V1.0-AWQ)
	* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardCoder-Python-34B-V1.0-GPTQ)
	* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardCoder-Python-34B-V1.0-GGUF)
	* [WizardLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0)
	<!-- repositories-available end -->

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

	```
	Below is an instruction that describes a task. Write a response that appropriately completes the request.

	### Instruction:
	{prompt}

	### Response:

	```

	<!-- prompt-template end -->


	<!-- README_AWQ.md-provided-files start -->
	## Provided files and AWQ parameters

	For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM.

	Models are released as sharded safetensors files.

	\| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \|
	\| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \|
	\| [main](https://huggingface.co/TheBloke/WizardCoder-Python-34B-V1.0-AWQ/tree/main) \| 4 \| 128 \| [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) \| 4096 \| 18.31 GB

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

	<!-- README_AWQ.md-use-from-vllm start -->
	## Serving this model from vLLM

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

	- When using vLLM as a server, pass the `--quantization awq` parameter, for example:

	```shell
	python3 python -m vllm.entrypoints.api_server --model TheBloke/WizardCoder-Python-34B-V1.0-AWQ --quantization awq
	```

	When using vLLM from Python code, pass the `quantization=awq` parameter, for example:

	```python
	from vllm import LLM, SamplingParams

	prompts = [
	"Hello, my name is",
	"The president of the United States is",
	"The capital of France is",
	"The future of AI is",
	]
	sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

	llm = LLM(model="TheBloke/WizardCoder-Python-34B-V1.0-AWQ", quantization="awq")

	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-python start -->
	## How to use this AWQ model from Python code

	### Install the necessary packages

	Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later

	```shell
	pip3 install autoawq
	```

	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 .
	```

	### You can then try the following example code

	```python
	from awq import AutoAWQForCausalLM
	from transformers import AutoTokenizer

	model_name_or_path = "TheBloke/WizardCoder-Python-34B-V1.0-AWQ"

	# Load model
	model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True,
	trust_remote_code=False, safetensors=True)
	tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False)

	prompt = "Tell me about AI"
	prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.

	### Instruction:
	{prompt}

	### Response:

	'''

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

	tokens = tokenizer(
	prompt_template,
	return_tensors='pt'
	).input_ids.cuda()

	# Generate output
	generation_output = model.generate(
	tokens,
	do_sample=True,
	temperature=0.7,
	top_p=0.95,
	top_k=40,
	max_new_tokens=512
	)

	print("Output: ", tokenizer.decode(generation_output[0]))

	# Inference can also be done using transformers' pipeline
	from transformers import 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_AWQ.md-use-from-python end -->

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

	The files provided are tested to work with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm).

	[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781).
	<!-- 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: 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: WizardLM's WizardCoder Python 34B V1.0


	<p align="center">
	🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
	</p>
	<p align="center">
	👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
	</p>

	## News

	- 🔥🔥🔥[2023/08/26] We released WizardCoder-Python-34B-V1.0 , which achieves the 73.2 pass@1 and surpasses GPT4 (2023/03/15), ChatGPT-3.5, and Claude2 on the [HumanEval Benchmarks](https://github.com/openai/human-eval).
	- [2023/06/16] We released WizardCoder-15B-V1.0 , which achieves the 57.3 pass@1 and surpasses Claude-Plus (+6.8), Bard (+15.3) and InstructCodeT5+ (+22.3) on the [HumanEval Benchmarks](https://github.com/openai/human-eval).

	❗Note: There are two HumanEval results of GPT4 and ChatGPT-3.5. The 67.0 and 48.1 are reported by the official GPT4 Report (2023/03/15) of [OpenAI](https://arxiv.org/abs/2303.08774). The 82.0 and 72.5 are tested by ourselves with the latest API (2023/08/26).


	\| Model \| Checkpoint \| Paper \| HumanEval \| MBPP \| Demo \| License \|
	\| ----- \|------\| ---- \|------\|-------\| ----- \| ----- \|
	\| WizardCoder-Python-34B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 73.2 \| 61.2 \| [Demo](http://47.103.63.15:50085/) \| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> \|
	\| WizardCoder-15B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 59.8 \|50.6 \| -- \| <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> \|
	\| WizardCoder-Python-13B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 64.0 \| 55.6 \| -- \| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> \|
	\| WizardCoder-3B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-3B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 34.8 \|37.4 \| [Demo](http://47.103.63.15:50086/) \| <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> \|
	\| WizardCoder-1B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-1B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> \| 23.8 \|28.6 \| -- \| <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> \|


	- Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
	- Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is 24.8 points higher than the SOTA open-source LLM, and achieves 22.7 pass@1 on the [MATH Benchmarks](https://github.com/hendrycks/math), which is 9.2 points higher than the SOTA open-source LLM.

	<font size=4>

	\| Model \| Checkpoint \| Paper \| GSM8k \| MATH \|Online Demo\| License\|
	\| ----- \|------\| ---- \|------\|-------\| ----- \| ----- \|
	\| WizardMath-70B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 81.6 \| 22.7 \|[Demo](http://47.103.63.15:50083/)\| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> \|
	\| WizardMath-13B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 63.9 \| 14.0 \|[Demo](http://47.103.63.15:50082/)\| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> \|
	\| WizardMath-7B-V1.0 \| 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> \| 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>\| 54.9 \| 10.7 \| [Demo ](http://47.103.63.15:50080/)\| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>\|
	</font>


	- [08/09/2023] We released WizardLM-70B-V1.0 model. Here is [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-70B-V1.0).

	<font size=4>


	\| <sup>Model</sup> \| <sup>Checkpoint</sup> \| <sup>Paper</sup> \|<sup>MT-Bench</sup> \| <sup>AlpacaEval</sup> \| <sup>GSM8k</sup> \| <sup>HumanEval</sup> \| <sup>License</sup>\|
	\| ----- \|------\| ---- \|------\|-------\| ----- \| ----- \| ----- \|
	\| <sup>WizardLM-70B-V1.0</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-70B-V1.0" target="_blank">HF Link</a> </sup>\|<sup>📃Coming Soon</sup>\| <sup>7.78</sup> \| <sup>92.91%</sup> \|<sup>77.6%</sup> \| <sup> 50.6</sup>\|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> \|
	\| <sup>WizardLM-13B-V1.2</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup>\| \| <sup>7.06</sup> \| <sup>89.17%</sup> \|<sup>55.3%</sup> \| <sup>36.6 </sup>\|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> \|
	\| <sup>WizardLM-13B-V1.1</sup> \|<sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup> \| \| <sup>6.76</sup> \|<sup>86.32%</sup> \| \| <sup>25.0 </sup>\| <sup>Non-commercial</sup>\|
	\| <sup>WizardLM-30B-V1.0</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup> \| \| <sup>7.01</sup> \| \| \| <sup>37.8 </sup>\| <sup>Non-commercial</sup> \|
	\| <sup>WizardLM-13B-V1.0</sup> \| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup> \| \| <sup>6.35</sup> \| <sup>75.31%</sup> \| \| <sup> 24.0 </sup> \| <sup>Non-commercial</sup>\|
	\| <sup>WizardLM-7B-V1.0 </sup>\| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup> \|<sup> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup>\| \| \| \|<sup>19.1 </sup>\|<sup> Non-commercial</sup>\|
	</font>


	## Comparing WizardCoder-Python-34B-V1.0 with Other LLMs.

	🔥 The following figure shows that our WizardCoder-Python-34B-V1.0 attains the second position in this benchmark, surpassing GPT4 (2023/03/15, 73.2 vs. 67.0), ChatGPT-3.5 (73.2 vs. 72.5) and Claude2 (73.2 vs. 71.2).

	<p align="center" width="100%">
	<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/WizardCoder/imgs/compare_sota.png" alt="WizardCoder" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
	</p>

	## Prompt Format
	```
	"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
	```

	## Inference Demo Script

	We provide the inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).

	## Citation

	Please cite the repo if you use the data, method or code in this repo.

	```
	@article{luo2023wizardcoder,
	title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct},
	author={Luo, Ziyang and Xu, Can and Zhao, Pu and Sun, Qingfeng and Geng, Xiubo and Hu, Wenxiang and Tao, Chongyang and Ma, Jing and Lin, Qingwei and Jiang, Daxin},
	journal={arXiv preprint arXiv:2306.08568},
	year={2023}
	}
	```