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Model: mradermacher/Einstein-v4-phi2-GGUF Source: Original Platform
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README.md
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---
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base_model: Weyaxi/Einstein-v4-phi2
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datasets:
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- allenai/ai2_arc
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- camel-ai/physics
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- camel-ai/chemistry
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- camel-ai/biology
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- camel-ai/math
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- metaeval/reclor
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- openbookqa
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- mandyyyyii/scibench
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- derek-thomas/ScienceQA
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- TIGER-Lab/ScienceEval
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- jondurbin/airoboros-3.2
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- LDJnr/Capybara
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- Cot-Alpaca-GPT4-From-OpenHermes-2.5
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- STEM-AI-mtl/Electrical-engineering
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- knowrohit07/saraswati-stem
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- sablo/oasst2_curated
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- glaiveai/glaive-code-assistant
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- lmsys/lmsys-chat-1m
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- TIGER-Lab/MathInstruct
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- bigbio/med_qa
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- meta-math/MetaMathQA-40K
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- openbookqa
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- piqa
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- metaeval/reclor
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- derek-thomas/ScienceQA
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- scibench
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- sciq
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- Open-Orca/SlimOrca
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- migtissera/Synthia-v1.3
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- TIGER-Lab/ScienceEval
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language:
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- en
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library_name: transformers
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license: other
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quantized_by: mradermacher
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tags:
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- axolotl
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- generated_from_trainer
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- phi
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- phi2
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- einstein
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- instruct
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- finetune
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- chatml
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- gpt4
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- synthetic data
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- science
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- physics
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- chemistry
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- biology
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- math
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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/Weyaxi/Einstein-v4-phi2
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<!-- provided-files -->
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Einstein-v4-phi2-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/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.Q2_K.gguf) | Q2_K | 1.2 | |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.Q3_K_S.gguf) | Q3_K_S | 1.4 | |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.IQ4_XS.gguf) | IQ4_XS | 1.6 | |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.Q3_K_L.gguf) | Q3_K_L | 1.7 | |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.Q4_K_M.gguf) | Q4_K_M | 1.8 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.Q5_K_S.gguf) | Q5_K_S | 2.0 | |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.Q5_K_M.gguf) | Q5_K_M | 2.1 | |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.Q6_K.gguf) | Q6_K | 2.4 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.Q8_0.gguf) | Q8_0 | 3.1 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/Einstein-v4-phi2-GGUF/resolve/main/Einstein-v4-phi2.f16.gguf) | f16 | 5.7 | 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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