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Llamacpp quants
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metadata
license: apache-2.0
datasets:
  - berkeley-nest/Nectar
language:
  - en
library_name: transformers
tags:
  - reward model
  - RLHF
  - RLAIF
quantized_by: bartowski
pipeline_tag: text-generation

Llamacpp Quantizations of Starling-LM-7B-beta

Using llama.cpp release b2440 for quantization.

Original model: https://huggingface.co./Nexusflow/Starling-LM-7B-beta

Download a file (not the whole branch) from below:

Filename Quant type File Size Description
Starling-LM-7B-beta-Q8_0.gguf Q8_0 7.69GB Extremely high quality, generally unneeded but max available quant.
Starling-LM-7B-beta-Q6_K.gguf Q6_K 5.94GB Very high quality, near perfect, recommended.
Starling-LM-7B-beta-Q5_K_M.gguf Q5_K_M 5.13GB High quality, very usable.
Starling-LM-7B-beta-Q5_K_S.gguf Q5_K_S 4.99GB High quality, very usable.
Starling-LM-7B-beta-Q5_0.gguf Q5_0 4.99GB High quality, older format, generally not recommended.
Starling-LM-7B-beta-Q4_K_M.gguf Q4_K_M 4.36GB Good quality, similar to 4.25 bpw.
Starling-LM-7B-beta-Q4_K_S.gguf Q4_K_S 4.14GB Slightly lower quality with small space savings.
Starling-LM-7B-beta-IQ4_NL.gguf IQ4_NL 4.15GB Good quality, similar to Q4_K_S, new method of quanting,
Starling-LM-7B-beta-IQ4_XS.gguf IQ4_XS 3.94GB Decent quality, new method with similar performance to Q4.
Starling-LM-7B-beta-Q4_0.gguf Q4_0 4.10GB Decent quality, older format, generally not recommended.
Starling-LM-7B-beta-IQ3_M.gguf IQ3_M 3.28GB Medium-low quality, new method with decent performance.
Starling-LM-7B-beta-IQ3_S.gguf IQ3_S 3.18GB Lower quality, new method with decent performance, recommended over Q3 quants.
Starling-LM-7B-beta-Q3_K_L.gguf Q3_K_L 3.82GB Lower quality but usable, good for low RAM availability.
Starling-LM-7B-beta-Q3_K_M.gguf Q3_K_M 3.51GB Even lower quality.
Starling-LM-7B-beta-Q3_K_S.gguf Q3_K_S 3.16GB Low quality, not recommended.
Starling-LM-7B-beta-Q2_K.gguf Q2_K 2.71GB Extremely low quality, not recommended.

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