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34B Beta 4.0bpw H6 EXL2 by LoneStriker

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34B Beta 4.0bpw H6 EXL2 Benchmarks

LLME Score: 0.18084
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

34B Beta 4.0bpw H6 EXL2 Parameters and Internals

Model Type 
Causal Language Model
Use Cases 
Considerations:
Avoid using "accelerated inference frameworks" like VLLM due to precision issues leading to significant output quality degradation. Use Transformers for inference, or consider using q8_0 quantization with llama.cpp temporarily for faster inference.
Additional Notes 
Some issues with model weights in terms of precision, to be fixed in the next version update. Repetition penalty should not be used. Re-alignment on synthetic datasets has changed distribution significantly from original datasets.
LLM Name34B Beta 4.0bpw H6 EXL2
Repository 🤗https://huggingface.co/LoneStriker/34b-beta-4.0bpw-h6-exl2 
Model Size34b
Required VRAM18.1 GB
Updated2024-11-21
MaintainerLoneStriker
Model Typellama
Model Files  8.6 GB: 1-of-3   8.6 GB: 2-of-3   0.9 GB: 3-of-3
Quantization Typeexl2
Model ArchitectureLlamaForCausalLM
Licensegpl-3.0
Context Length200000
Model Max Length200000
Transformers Version4.37.2
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size64000
Torch Data Typebfloat16
34B Beta 4.0bpw H6 EXL2 (LoneStriker/34b-beta-4.0bpw-h6-exl2)

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Note: green Score (e.g. "73.2") means that the model is better than LoneStriker/34b-beta-4.0bpw-h6-exl2.

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241110
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