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Yi 34B 200K DARE Merge V5 AWQ by TheBloke

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Yi 34B 200K DARE Merge V5 AWQ is an open-source language model by TheBloke. Features: 34b LLM, VRAM: 19.3GB, Context: 195K, License: other, Quantized, Merged, LLM Explorer Score: 0.11.

  Merged Model   4-bit   Autotrain compatible   Awq Base model:brucethemoose/yi-34... Base model:quantized:brucethem...   En   Llama   Quantized   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/TheBloke/Yi-34B-200K-DARE-merge-v5-AWQ 

Yi 34B 200K DARE Merge V5 AWQ Benchmarks

LLME Score: 0.11032
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Yi 34B 200K DARE Merge V5 AWQ (TheBloke/Yi-34B-200K-DARE-merge-v5-AWQ)
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Yi 34B 200K DARE Merge V5 AWQ Parameters and Internals

Model Type 
yi, text-generation
Additional Notes 
Yi tends to run "hot" by default, and it really needs MinP to cull the huge vocabulary. 24GB GPUs can run Yi-34B-200K models at 45K-75K context with exllamav2.
Input Output 
Input Format:
SYSTEM: {system_message} USER: {prompt} ASSISTANT:
Release Notes 
Notes:
Various densities were tested with perplexity tests and long context prompts. Relatively high densities seem to perform better, contrary to the findings of the Super Mario paper. This particular version is merged with more than the "recommended" max density of 0.5. It seems to result in even better perplexity, but I'm not sure if this translates to better output. Weights that add up to 1 seem to be optimal. Dare Ties is also resulting in seemingly better, lower perplexity merges than a regular ties merge, task arithmetic, or a slerp merge. SUS Chat is not a 200K model, hence it was merged at a very low density to try and preserve Yi 200K's long context performance while "keeping" some of SUS parameters.
LLM NameYi 34B 200K DARE Merge V5 AWQ
Repository 🤗https://huggingface.co/TheBloke/Yi-34B-200K-DARE-merge-v5-AWQ 
Model NameYi 34B 200K DARE Merge v5
Model Creatorbrucethemoose
Base Model(s)  Yi 34B 200K DARE Merge V5   brucethemoose/Yi-34B-200K-DARE-merge-v5
Merged ModelYes
Model Size34b
Required VRAM19.3 GB
Updated2025-09-23
MaintainerTheBloke
Model Typellama
Model Files  10.0 GB: 1-of-2   9.3 GB: 2-of-2
Supported Languagesen
AWQ QuantizationYes
Quantization Typeawq
Model ArchitectureLlamaForCausalLM
Licenseother
Context Length200000
Model Max Length200000
Transformers Version4.35.2
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size64000
Torch Data Typefloat16

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Note: green Score (e.g. "73.2") means that the model is better than TheBloke/Yi-34B-200K-DARE-merge-v5-AWQ.

Rank the Yi 34B 200K DARE Merge V5 AWQ 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  

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