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, HF Score: 64, LLM Explorer Score: 0.24, Arc: 65.8, HellaSwag: 82.1, MMLU: 75.6, TruthfulQA: 42.6, WinoGrande: 82.9, GSM8K: 34.9.

Arxiv:2311.16502 Arxiv:2401.11944 Arxiv:2403.04652 Autotrain compatible 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

IFEval: 15.42 vs 88 (so35)^-82.5%

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%

TruthfulQA: 42.6 vs 59 (gpt4)^-27.8%

WinoGrande: 82.87 vs 87.5 (gpt4)^-5.3%

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

MATH Lvl 5: 5.74

LLME Score: 0.24402

^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	2025-10-21
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	1181	14 GB
Yi 34B 200K AWQ	9	212	19 GB
Yi 34B 200K GPTQ	3	10	18 GB
Yi 34B 200K AWQ	1	7	19 GB

Best Alternatives to Yi 34B 200K

Best Alternatives	Context / RAM	Downloads	Likes
Bagel Hermes 34B Slerp	195K / 68.9 GB	8712	1
34B Beta	195K / 69.2 GB	7868	64
Smaug 34B V0.1	195K / 69.2 GB	8634	61
Casual Magnum 34B	195K / 68.8 GB	5	1
Bagel 34B V0.2	195K / 68.7 GB	2752	41
Yi 34B 200K AEZAKMI V2	195K / 69.2 GB	1872	12
Faro Yi 34B	195K / 69.2 GB	8679	6
Bagel DPO 34B V0.5	195K / 68.7 GB	8055	17
Smaug 34B V0.1 ExPO	195K / 69.2 GB	7108	0
Luminex 34B V0.1	195K / 68.9 GB	9240	8

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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