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  <!-- ### quantize_version: 2 -->
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  <!-- ### output_tensor_quantised: 1 -->
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  <!-- ### convert_type: hf -->
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  <!-- ### vocab_type: -->
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  <!-- ### tags: -->
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  static quants of https://huggingface.co/darkcloudai/huskylm-2.5-8b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: darkcloudai/huskylm-2.5-8b
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+ datasets:
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+ - darkcloudai-smallmodel-frontieredition
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+ - darkcloudai-webdriver-redditcrawl-2023
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+ - darkcloudai-unalignment-truthfulness
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+ - darkcloudai-generaldpo
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+ - ai2_arc
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+ - allenai/ultrafeedback_binarized_cleaned
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+ - argilla/distilabel-intel-orca-dpo-pairs
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+ - jondurbin/airoboros-3.2
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+ - codeparrot/apps
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+ - facebook/belebele
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+ - bluemoon-fandom-1-1-rp-cleaned
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+ - boolq
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+ - camel-ai/biology
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+ - camel-ai/chemistry
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+ - camel-ai/math
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+ - camel-ai/physics
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+ - jondurbin/contextual-dpo-v0.1
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+ - jondurbin/gutenberg-dpo-v0.1
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+ - jondurbin/py-dpo-v0.1
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+ - jondurbin/truthy-dpo-v0.1
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+ - LDJnr/Capybara
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+ - jondurbin/cinematika-v0.1
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+ - WizardLM/WizardLM_evol_instruct_70k
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+ - glaiveai/glaive-function-calling-v2
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+ - jondurbin/gutenberg-dpo-v0.1
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+ - grimulkan/LimaRP-augmented
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+ - lmsys/lmsys-chat-1m
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+ - ParisNeo/lollms_aware_dataset
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+ - TIGER-Lab/MathInstruct
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+ - Muennighoff/natural-instructions
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+ - openbookqa
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+ - kingbri/PIPPA-shareGPT
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+ - piqa
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+ - Vezora/Tested-22k-Python-Alpaca
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+ - ropes
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+ - cakiki/rosetta-code
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+ - Open-Orca/SlimOrca
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+ - b-mc2/sql-create-context
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+ - squad_v2
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+ - mattpscott/airoboros-summarization
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+ - migtissera/Synthia-v1.3
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+ - unalignment/toxic-dpo-v0.2
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+ - WhiteRabbitNeo/WRN-Chapter-1
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+ - WhiteRabbitNeo/WRN-Chapter-2
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+ - winogrande
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+ language:
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+ - en
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+ library_name: transformers
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+ license: llama3
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+ quantized_by: mradermacher
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+ tags:
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+ - llama-3
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+ - huskylm
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+ - darkcloudai
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+ ---
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+ ## About
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+
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  <!-- ### quantize_version: 2 -->
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  <!-- ### output_tensor_quantised: 1 -->
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  <!-- ### convert_type: hf -->
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  <!-- ### vocab_type: -->
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  <!-- ### tags: -->
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  static quants of https://huggingface.co/darkcloudai/huskylm-2.5-8b
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+
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+ <!-- provided-files -->
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+ weighted/imatrix quants are available at https://huggingface.co/mradermacher/huskylm-2.5-8b-i1-GGUF
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+ ## Usage
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+
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+ If you are unsure how to use GGUF files, refer to one of [TheBloke's
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+ READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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+ more details, including on how to concatenate multi-part files.
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+
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+ ## Provided Quants
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+
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+ (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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+
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+ | Link | Type | Size/GB | Notes |
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+ |:-----|:-----|--------:|:------|
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.Q2_K.gguf) | Q2_K | 3.3 | |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.IQ3_M.gguf) | IQ3_M | 3.9 | |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
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+ | [GGUF](https://huggingface.co/mradermacher/huskylm-2.5-8b-GGUF/resolve/main/huskylm-2.5-8b.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
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+
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+ Here is a handy graph by ikawrakow comparing some lower-quality quant
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+ types (lower is better):
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+
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+ ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
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+
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+ And here are Artefact2's thoughts on the matter:
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+ https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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+
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+ ## FAQ / Model Request
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+
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+ See https://huggingface.co/mradermacher/model_requests for some answers to
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+ questions you might have and/or if you want some other model quantized.
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+
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+ ## Thanks
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+
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+ I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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+ me use its servers and providing upgrades to my workstation to enable
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+ this work in my free time.
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+
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+ <!-- end -->