What are the hardware requirements for Yi 1.5 34B Chat AWQ?

Yi 1.5 34B Chat AWQ requires approximately 19.2 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Yi 1.5 34B Chat AWQ and how large is it?

Yi 1.5 34B Chat AWQ is developed by modelscope, a model with 34b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Yi 1.5 34B Chat AWQ?

Yi 1.5 34B Chat AWQ is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Yi 1.5 34B Chat AWQ by modelscope — VRAM 19.2GB, 4K context

Name: Yi 1.5 34B Chat AWQ
Author: modelscope

Yi 1.5 34B Chat AWQ is an open-source language model by modelscope. Features: 34b LLM, VRAM: 19.2GB, Context: 4K, License: apache-2.0, Quantized, LLM Explorer Score: 0.12.

4-bit Awq Conversational En Endpoints compatible Int4 Llama Pytorch Quantized Region:us Safetensors Sharded Tensorflow Yi1.5-34b-chat Zh

Model Card on HF 🤗: https://huggingface.co/modelscope/Yi-1.5-34B-Chat-AWQ

Yi 1.5 34B Chat AWQ Parameters and Internals

Model Type

text-generation

Additional Notes

We use the modelscope swift repository to perform AWQ 4bit quantization.

Supported Languages

en (proficient), zh (proficient)

LLM Name	Yi 1.5 34B Chat AWQ
Repository 🤗	https://huggingface.co/modelscope/Yi-1.5-34B-Chat-AWQ
Base Model(s)	01-ai/Yi-1.5-34B-Chat 01-ai/Yi-1.5-34B-Chat
Model Size	34b
Required VRAM	19.2 GB
Updated	2026-08-03
Maintainer	modelscope
Model Type	llama
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 4.3 GB: 4-of-4
AWQ Quantization	Yes
Quantization Type	awq
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.39.3
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	64000
Torch Data Type	float16

Rank the Yi 1.5 34B Chat AWQ capabilities

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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

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