What are the hardware requirements for Breeze 7B 32K Instruct V1.0 AWQ?

Breeze 7B 32K Instruct V1.0 AWQ requires approximately 4.7 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Breeze 7B 32K Instruct V1.0 AWQ and how large is it?

Breeze 7B 32K Instruct V1.0 AWQ is developed by chienweichang, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Breeze 7B 32K Instruct V1.0 AWQ perform on standard benchmarks?

Breeze 7B 32K Instruct V1.0 AWQ has the following published scores: MMLU 62.11. Compare against reference models on this page or on the LLM Explorer leaderboards.

Breeze 7B 32K Instruct V1.0 AWQ by chienweichang — VRAM 4.7GB, 32K context

Name: Breeze 7B 32K Instruct V1.0 AWQ
Author: chienweichang

Breeze 7B 32K Instruct V1.0 AWQ is an open-source language model by chienweichang. Features: 7b LLM, VRAM: 4.7GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.14, Arc: 61.4, HellaSwag: 82.6, MMLU: 62.1, GSM8K: 43.1.

4-bit Autotrain compatible Awq Conversational En Endpoints compatible Instruct Mistral Quantized Region:us Safetensors Zh

Model Card on HF 🤗: https://huggingface.co/chienweichang/Breeze-7B-32k-Instruct-v1_0-AWQ

Breeze 7B 32K Instruct V1.0 AWQ Parameters and Internals

Additional Notes

This model supports AWQ, a low-bit weight quantization method, providing efficient 4-bit quantization especially for Transformers-based inference. It can be used via several libraries and frameworks such as Text Generation Webui, vLLM, and Transformers.

Supported Languages

zh (presumed), en (presumed)

LLM Name	Breeze 7B 32K Instruct V1.0 AWQ
Repository 🤗	https://huggingface.co/chienweichang/Breeze-7B-32k-Instruct-v1_0-AWQ
Base Model(s)	Breeze 7B Instruct V1.0 MediaTek-Research/Breeze-7B-Instruct-v1_0
Model Size	7b
Required VRAM	4.7 GB
Updated	2024-12-09
Maintainer	chienweichang
Model Type	mistral
Instruction-Based	Yes
Model Files	4.7 GB
Supported Languages	zh en
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.38.2
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	62464
Torch Data Type	float16

Rank the Breeze 7B 32K Instruct V1.0 AWQ capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Breeze 7B 32K Instruct V1.0 AWQ

Best Alternatives	Context / RAM	Downloads	Likes
Mistral 7B Instruct V0.2 AWQ	32K / 4.2 GB	6	0
Mistral 7B Instruct V0.2 AWQ	32K / 4.2 GB	298182	52
Mistral 7B Instruct V0.3 AWQ	32K / 4.2 GB	5633	10
Mistral 7B Instruct V0.1 AWQ	32K / 4.2 GB	15461	2
Breeze 7B Instruct V1.0 AWQ	32K / 4.6 GB	5	0
Breeze 7B Instruct V1.0 AWQ	32K / 4.6 GB	14	1
Mistral 7B Instruct V0.3 AWQ	32K / 4.2 GB	628	1
Mistral 7B Instruct V0.3 AWQ	32K / 4.2 GB	5	0
...Instruct V0.2 AWQ 4bit Smashed	32K / 4.2 GB	8	1
Mistral 7B Instruct 32K AWQ	32K / 4.2 GB	4	1

Note: green Score (e.g. "73.2") means that the model is better than chienweichang/Breeze-7B-32k-Instruct-v1_0-AWQ.