What are the hardware requirements for ChatAllInOne Yi 34B 200K V1?

ChatAllInOne Yi 34B 200K V1 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 ChatAllInOne Yi 34B 200K V1 and how large is it?

ChatAllInOne Yi 34B 200K V1 is developed by DrNicefellow, 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 ChatAllInOne Yi 34B 200K V1 perform on standard benchmarks?

ChatAllInOne Yi 34B 200K V1 has the following published scores: MMLU 73.95. Compare against reference models on this page or on the LLM Explorer leaderboards.

ChatAllInOne Yi 34B 200K V1 by DrNicefellow — VRAM 69.2GB, 195K context

Name: ChatAllInOne Yi 34B 200K V1
Author: DrNicefellow

ChatAllInOne Yi 34B 200K V1 is an open-source language model by DrNicefellow. Features: 34b LLM, VRAM: 69.2GB, Context: 195K, License: apache-2.0, LLM Explorer Score: 0.12, Arc: 66, HellaSwag: 84.6, MMLU: 74, GSM8K: 59.5.

Dataset:drnicefellow/chat-all-... Endpoints compatible Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/DrNicefellow/ChatAllInOne-Yi-34B-200K-V1

ChatAllInOne Yi 34B 200K V1 Parameters and Internals

Model Type

chat

Additional Notes

Enhanced understanding and generation of conversational language. Improved performance in diverse chat scenarios. Fine-tuned to maintain context and coherence over longer dialogues.

Training Details

Data Sources:

CHAT-ALL-IN-ONE-v1

Methodology:

Fine-tuning using QLoRA with unsloth tool

Input Output

Input Format:

Vicuna 1.1

LLM Name	ChatAllInOne Yi 34B 200K V1
Repository 🤗	https://huggingface.co/DrNicefellow/ChatAllInOne-Yi-34B-200K-V1
Model Size	34b
Required VRAM	69.2 GB
Updated	2026-08-16
Maintainer	DrNicefellow
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	4.37.0
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	64000
Torch Data Type	bfloat16

Rank the ChatAllInOne Yi 34B 200K V1 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

Quantized Models of the ChatAllInOne Yi 34B 200K V1

Model	Likes	Downloads	VRAM
...atAllInOne Yi 34B 200K V1 GGUF	0	361	12 GB

Best Alternatives to ChatAllInOne Yi 34B 200K V1

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 DrNicefellow/ChatAllInOne-Yi-34B-200K-V1.