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

Yi 34B 200K requires approximately 68.9 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 and how large is it?

Yi 34B 200K is developed by 01-ai, 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 perform on standard benchmarks?

Yi 34B 200K has the following published scores: MMLU 75.56. Compare against reference models on this page or on the LLM Explorer leaderboards.

Yi 34B 200K by 01-ai — VRAM 68.9GB, 195K context

Name: Yi 34B 200K
Author: 01-ai

Yi 34B 200K is an open-source language model by 01-ai. Features: 34b LLM, VRAM: 68.9GB, Context: 195K, License: apache-2.0, LLM Explorer Score: 0.22, Arc: 65.8, HellaSwag: 82.1, MMLU: 75.6, GSM8K: 34.9.

Arxiv:2311.16502 Arxiv:2401.11944 Arxiv:2403.04652 Deploy:azure Endpoints compatible Llama Pytorch Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/01-ai/Yi-34B-200K

Yi 34B 200K Benchmarks

ARC: 65.78 vs 96.7 (so35)^-32%

HellaSwag: 82.06 vs 95.3 (gpt4)^-13.9%

MMLU: 75.56 vs 88.3 (so35)^-14.4%

GSM8K: 34.87 vs 96.4 (so35)^-63.8%

LLME Score: 0.22001

^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 200K Parameters and Internals

Model Type

Chat model, Text generation

Use Cases

Areas:

Chat applications, Creative content generation

Applications:

Commercial applications, Research, Educational tools

Primary Use Cases:

Chatbots, Virtual assistants, Story generation

Limitations:

Potential for hallucination, May produce inconsistent outputs

Considerations:

Adjust generation parameters for desired output qualities.

Additional Notes

Models do not directly use Llama's weights; unique datasets and training infrastructure emphasize Yi's independent development.

Supported Languages

English (Fluent), Chinese (Fluent)

Training Details

Data Sources:

Trainer Multilingual Corpora, 3T Tokens

Data Volume:

3T Multilingual Corpus

Methodology:

Transformer-based architecture

Context Length:

200000

Training Time:

Not specified

Hardware Used:

NVIDIA A800 (80GB), 4090 GPU

Model Architecture:

Based on Llama's architecture

Responsible Ai Considerations

Fairness:

Addressed during model development.

Transparency:

Standard Transformer architecture; detailed in tech report.

Accountability:

01.AI

Mitigation Strategies:

Use of Supervised Fine-Tuning for better accuracy.

Input Output

Input Format:

Interactive prompt conversation

Accepted Modalities:

Text

Output Format:

Text responses or follow-ups

Performance Tips:

Calibrate temperature, top_p, top_k settings for desired response diversity.

Release Notes

Version:

1.0

Date:

2023-11-23

Notes:

Initial open-source release of chat model, supporting both 4-bit and 8-bit quantizations.

Version:

2.0

Date:

2023-12-19

Notes:

Improved performance in coding, math, and reasoning with larger context capabilities.

LLM Name	Yi 34B 200K
Repository 🤗	https://huggingface.co/01-ai/Yi-34B-200K
Model Size	34b
Required VRAM	68.9 GB
Updated	2026-06-07
Maintainer	01-ai
Model Type	llama
Model Files	10.0 GB: 1-of-7 9.9 GB: 2-of-7 9.8 GB: 3-of-7 9.8 GB: 4-of-7 9.8 GB: 5-of-7 9.9 GB: 6-of-7 9.7 GB: 7-of-7 10.0 GB: 1-of-7 9.9 GB: 2-of-7 9.8 GB: 3-of-7 9.8 GB: 4-of-7 9.8 GB: 5-of-7 9.9 GB: 6-of-7 9.7 GB: 7-of-7
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

Model	Likes	Downloads	VRAM
Yi 34B 200K GGUF	29	1096	14 GB
Yi 34B 200K AWQ	9	212	19 GB
Yi 34B 200K GPTQ	3	20	18 GB
Yi 34B 200K AWQ	1	10	19 GB

Best Alternatives to Yi 34B 200K

Best Alternatives	Context / RAM	Downloads	Likes
Bagel Hermes 34B Slerp	195K / 68.9 GB	8375	1
34B Beta	195K / 69.2 GB	8242	66
Smaug 34B V0.1	195K / 69.2 GB	8479	64
Casual Magnum 34B	195K / 68.8 GB	4	1
Bagel 34B V0.2	195K / 68.7 GB	943	41
Yi 34B 200K AEZAKMI V2	195K / 69.2 GB	223	12
Bagel DPO 34B V0.5	195K / 68.7 GB	8242	17
Faro Yi 34B	195K / 69.2 GB	8476	6
Smaug 34B V0.1 ExPO	195K / 69.2 GB	7738	0
Luminex 34B V0.1	195K / 68.9 GB	8236	8

Note: green Score (e.g. "73.2") means that the model is better than 01-ai/Yi-34B-200K.

Rank the Yi 34B 200K 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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Yi 34B 200K by 01-ai