What are the hardware requirements for Yi 34B V2?

Yi 34B V2 requires approximately 68.9 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Yi 34B V2 and how large is it?

Yi 34B V2 is developed by mncai, a model with 34b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Yi 34B V2 perform on standard benchmarks?

Yi 34B V2 has the following published scores: MMLU 75.64. Compare against reference models on this page or on the LLM Explorer leaderboards.

Yi 34B V2 by mncai — VRAM 68.9GB, 8K context

Name: Yi 34B V2
Author: mncai

Yi 34B V2 is an open-source language model by mncai. Features: 34b LLM, VRAM: 68.9GB, Context: 8K, License: apache-2.0, LLM Explorer Score: 0.12, Arc: 66.1, HellaSwag: 85, MMLU: 75.6, GSM8K: 65.

Endpoints compatible Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mncai/yi-34B-v2

Yi 34B V2 Benchmarks

ARC: 66.13 vs 96.7 (so35)^-31.6%

HellaSwag: 85 vs 95.3 (gpt4)^-10.8%

MMLU: 75.64 vs 88.3 (so35)^-14.3%

GSM8K: 64.97 vs 96.4 (so35)^-32.6%

LLME Score: 0.11814

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

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Yi 34B V2 Parameters and Internals

Model Type

instruction tuned

LLM Name	Yi 34B V2
Repository 🤗	https://huggingface.co/mncai/yi-34B-v2
Model Size	34b
Required VRAM	68.9 GB
Updated	2026-05-26
Maintainer	mncai
Model Type	llama
Model Files	9.8 GB: 1-of-8 9.8 GB: 2-of-8 9.8 GB: 3-of-8 9.8 GB: 4-of-8 9.8 GB: 5-of-8 9.8 GB: 6-of-8 9.8 GB: 7-of-8 0.3 GB: 8-of-8
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.35.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	64000
Torch Data Type	bfloat16

Best Alternatives to Yi 34B V2

Best Alternatives	Context / RAM	Downloads	Likes
Bagel Hermes 34B Slerp	195K / 68.9 GB	8371	1
34B Beta	195K / 69.2 GB	8242	66
Smaug 34B V0.1	195K / 69.2 GB	8421	64
Yi 34B 200K	195K / 68.9 GB	10338	320
Casual Magnum 34B	195K / 68.8 GB	4	1
Bagel 34B V0.2	195K / 68.7 GB	944	41
Yi 34B 200K AEZAKMI V2	195K / 69.2 GB	537	12
Smaug 34B V0.1 ExPO	195K / 69.2 GB	7738	0
Bagel DPO 34B V0.5	195K / 68.7 GB	8213	17
Faro Yi 34B	195K / 69.2 GB	8419	6

Note: green Score (e.g. "73.2") means that the model is better than mncai/yi-34B-v2.

Rank the Yi 34B V2 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Release v20260328a

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Yi 34B V2 by mncai

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Yi 34B V2 Benchmarks

Yi 34B V2 Parameters and Internals

Best Alternatives to Yi 34B V2

Rank the Yi 34B V2 Capabilities

What open-source LLMs or SLMs are you in search of? 53972 in total.