What are the hardware requirements for Yi 34B 200K DARE Megamerge V8?

Yi 34B 200K DARE Megamerge V8 requires approximately 68.8 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 DARE Megamerge V8 and how large is it?

Yi 34B 200K DARE Megamerge V8 is developed by brucethemoose, 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 DARE Megamerge V8 perform on standard benchmarks?

Yi 34B 200K DARE Megamerge V8 has the following published scores: MMLU 77.03. Compare against reference models on this page or on the LLM Explorer leaderboards.

Yi 34B 200K DARE Megamerge V8 by brucethemoose — VRAM 68.8GB, 195K context

Name: Yi 34B 200K DARE Megamerge V8
Author: brucethemoose

Yi 34B 200K DARE Megamerge V8 is an open-source language model by brucethemoose. Features: 34b LLM, VRAM: 68.8GB, Context: 195K, License: other, Merged, LLM Explorer Score: 0.11, Arc: 67.8, HellaSwag: 86.1, MMLU: 77, GSM8K: 65.4.

Merged Model Arxiv:2306.01708 Arxiv:2311.03099 En Endpoints compatible Llama Model-index Region:us Safetensors Sharded Tensorflow Yi

Model Card on HF 🤗: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8

Yi 34B 200K DARE Megamerge V8 Parameters and Internals

Model Type

text-generation

Additional Notes

Merged using DARE TIES method to handle models with up to 200,000 context efficiently. Specialized in merging multiple Yi models for improved performance.

Input Output

Input Format:

Orca-Vicuna template

Accepted Modalities:

text

Output Format:

text

Performance Tips:

Run at a lower temperature with 0.1 or higher MinP, a little repetition penalty, possibly mirostat with low tau.

LLM Name	Yi 34B 200K DARE Megamerge V8
Repository 🤗	https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8
Merged Model	Yes
Model Size	34b
Required VRAM	68.8 GB
Updated	2026-08-01
Maintainer	brucethemoose
Model Type	llama
Model Files	9.8 GB: 1-of-7 9.8 GB: 2-of-7 9.8 GB: 3-of-7 10.0 GB: 4-of-7 9.8 GB: 5-of-7 9.8 GB: 6-of-7 9.8 GB: 7-of-7
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	other
Context Length	200000
Model Max Length	200000
Transformers Version	4.35.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	64002
Torch Data Type	bfloat16

Rank the Yi 34B 200K DARE Megamerge V8 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

Quantized Models of the Yi 34B 200K DARE Megamerge V8

Model	Likes	Downloads	VRAM
...4B 200K DARE Megamerge V8 GGUF	14	3504	9 GB
...34B 200K DARE Megamerge V8 AWQ	2	10	19 GB
...4B 200K DARE Megamerge V8 GPTQ	3	4	18 GB

Best Alternatives to Yi 34B 200K DARE Megamerge V8

Best Alternatives	Context / RAM	Downloads	Likes
Bagel Hermes 34B Slerp	195K / 68.9 GB	8375	1
34B Beta	195K / 69.2 GB	8670	67
Smaug 34B V0.1	195K / 69.2 GB	8082	64
Casual Magnum 34B	195K / 68.8 GB	4	1
Yi 34B 200K	195K / 68.9 GB	9133	320
Bagel 34B V0.2	195K / 68.7 GB	259	41
Yi 34B 200K AEZAKMI V2	195K / 69.2 GB	57	12
Faro Yi 34B	195K / 69.2 GB	8208	6
Bagel DPO 34B V0.5	195K / 68.7 GB	8040	17
Smaug 34B V0.1 ExPO	195K / 69.2 GB	7812	0

Note: green Score (e.g. "73.2") means that the model is better than brucethemoose/Yi-34B-200K-DARE-megamerge-v8.