What are the hardware requirements for Yi 34B 200K AEZAKMI V2?

Yi 34B 200K AEZAKMI V2 requires approximately 69.2 GB of VRAM and supports a context window of 195K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Yi 34B 200K AEZAKMI V2 and how large is it?

Yi 34B 200K AEZAKMI V2 is developed by adamo1139, 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 200K AEZAKMI V2 perform on standard benchmarks?

Yi 34B 200K AEZAKMI V2 has the following published scores: MMLU 75.22. Compare against reference models on this page or on the LLM Explorer leaderboards.

Yi 34B 200K AEZAKMI V2 by adamo1139 — VRAM 69.2GB, 195K context

Name: Yi 34B 200K AEZAKMI V2
Author: adamo1139

Yi 34B 200K AEZAKMI V2 is an open-source language model by adamo1139. Features: 34b LLM, VRAM: 69.2GB, Context: 195K, License: apache-2.0, HF Score: 71, LLM Explorer Score: 0.19, Arc: 67.9, HellaSwag: 85.6, MMLU: 75.2, TruthfulQA: 56.7, WinoGrande: 81.6, GSM8K: 58.9.

Conversational Dataset:adamo1139/aezakmi v2 Endpoints compatible Finetuned Llama Model-index Region:us Safetensors Sharded Tensorflow Yi

Model Card on HF 🤗: https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2

Yi 34B 200K AEZAKMI V2 Benchmarks

IFEval: 45.55 vs 88 (so35)^-48.2%

ARC: 67.92 vs 96.7 (so35)^-29.8%

HellaSwag: 85.61 vs 95.3 (gpt4)^-10.2%

MMLU: 75.22 vs 88.3 (so35)^-14.8%

TruthfulQA: 56.74 vs 59 (gpt4)^-3.8%

WinoGrande: 81.61 vs 87.5 (gpt4)^-6.7%

GSM8K: 58.91 vs 96.4 (so35)^-38.9%

MATH Lvl 5: 5.66

LLME Score: 0.1913

^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)

Yi 34B 200K AEZAKMI V2 (adamo1139/Yi-34B-200K-AEZAKMI-v2)

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Yi 34B 200K AEZAKMI V2 Parameters and Internals

Model Type

text-generation

Use Cases

Limitations:

Don't expect it to be good at math, riddles or be crazy smart.

Additional Notes

Fine-tuned to avoid using typical language from RLHFed models such as 'It's important to remember.'

Training Details

Data Sources:

AEZAKMI_v2

Methodology:

Fine-tuning

Context Length:

24000

Training Time:

25 hours

Hardware Used:

single local RTX 3090 Ti

Input Output

Input Format:

ChatML format

Performance Tips:

Set repetition penalty to ~1.05; temperature 1.2 recommended.

LLM Name	Yi 34B 200K AEZAKMI V2
Repository 🤗	https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2
Model Size	34b
Required VRAM	69.2 GB
Updated	2026-04-16
Maintainer	adamo1139
Model Type	llama
Model Files	4.8 GB: 1-of-15 4.8 GB: 2-of-15 5.0 GB: 3-of-15 4.8 GB: 4-of-15 4.8 GB: 5-of-15 5.0 GB: 6-of-15 4.8 GB: 7-of-15 4.8 GB: 8-of-15 5.0 GB: 9-of-15 4.8 GB: 10-of-15 4.8 GB: 11-of-15 5.0 GB: 12-of-15 4.8 GB: 13-of-15 4.8 GB: 14-of-15 1.2 GB: 15-of-15
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	200000
Model Max Length	200000
Transformers Version	4.34.0
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	64000
Torch Data Type	bfloat16

Quantized Models of the Yi 34B 200K AEZAKMI V2

Model	Likes	Downloads	VRAM
Yi 34B 200K AEZAKMI V2 GGUF	3	75	14 GB
Yi 34B 200K AEZAKMI V2 AWQ	2	9	19 GB
Yi 34B 200K AEZAKMI V2 GPTQ	2	7	18 GB

Best Alternatives to Yi 34B 200K AEZAKMI V2

Best Alternatives	Context / RAM	Downloads	Likes
Bagel Hermes 34B Slerp	195K / 68.9 GB	8078	1
34B Beta	195K / 69.2 GB	8253	66
Smaug 34B V0.1	195K / 69.2 GB	8313	64
Yi 34B 200K	195K / 68.9 GB	10271	320
Casual Magnum 34B	195K / 68.8 GB	13	1
Bagel 34B V0.2	195K / 68.7 GB	2752	41
Smaug 34B V0.1 ExPO	195K / 69.2 GB	7738	0
Bagel DPO 34B V0.5	195K / 68.7 GB	8131	17
Faro Yi 34B	195K / 69.2 GB	8230	6
Luminex 34B V0.1	195K / 68.9 GB	7703	8

Note: green Score (e.g. "73.2") means that the model is better than adamo1139/Yi-34B-200K-AEZAKMI-v2.

Rank the Yi 34B 200K AEZAKMI 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 200K AEZAKMI V2 by adamo1139