138 lines
4.6 KiB
Markdown
138 lines
4.6 KiB
Markdown
---
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base_model: LeroyDyer/SpydazWeb_AI_HumanAGI_004
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datasets:
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- neoneye/base64-decode-v2
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- neoneye/base64-encode-v1
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- VuongQuoc/Chemistry_text_to_image
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- Kamizuru00/diagram_image_to_text
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- LeroyDyer/Chemistry_text_to_image_BASE64
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- LeroyDyer/AudioCaps-Spectrograms_to_Base64
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- LeroyDyer/winogroud_text_to_imaget_BASE64
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- LeroyDyer/chart_text_to_Base64
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- LeroyDyer/diagram_image_to_text_BASE64
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- mekaneeky/salt_m2e_15_3_instruction
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- mekaneeky/SALT-languages-bible
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- xz56/react-llama
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- BeIR/hotpotqa
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- arcee-ai/agent-data
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language:
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- en
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- sw
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- ig
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- so
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- es
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- ca
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- xh
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- zu
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- ha
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- tw
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- af
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- hi
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- bm
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- su
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library_name: transformers
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license: apache-2.0
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quantized_by: mradermacher
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- mistral
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- Mistral_Star
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- Mistral_Quiet
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- Mistral
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- Mixtral
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- Question-Answer
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- Token-Classification
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- Sequence-Classification
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- SpydazWeb-AI
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- chemistry
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- biology
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- legal
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- code
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- climate
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- medical
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- LCARS_AI_StarTrek_Computer
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- text-generation-inference
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- chain-of-thought
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- tree-of-knowledge
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- forest-of-thoughts
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- visual-spacial-sketchpad
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- alpha-mind
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- knowledge-graph
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- entity-detection
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- encyclopedia
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- wikipedia
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- stack-exchange
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- Reddit
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- Cyber-series
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- MegaMind
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- Cybertron
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- SpydazWeb
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- Spydaz
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- LCARS
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- star-trek
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- mega-transformers
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- Mulit-Mega-Merge
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- Multi-Lingual
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- Afro-Centric
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- African-Model
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- Ancient-One
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---
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## About
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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/LeroyDyer/SpydazWeb_AI_HumanAGI_004
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<!-- provided-files -->
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-i1-GGUF
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## Usage
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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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## Provided Quants
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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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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q2_K.gguf) | Q2_K | 2.8 | |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/SpydazWeb_AI_HumanAGI_004-GGUF/resolve/main/SpydazWeb_AI_HumanAGI_004.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
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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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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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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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## Thanks
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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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<!-- end -->
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