transformers gguf axolotl generated_from_trainer Mistral instruct finetune chatml gpt4 synthetic data science physics chemistry biology math en dataset:allenai/ai2_arc dataset:camel-ai/physics dataset:camel-ai/chemistry dataset:camel-ai/biology dataset:camel-ai/math dataset:metaeval/reclor dataset:openbookqa dataset:mandyyyyii/scibench dataset:derek-thomas/ScienceQA dataset:TIGER-Lab/ScienceEval dataset:jondurbin/airoboros-3.2 dataset:LDJnr/Capybara dataset:Cot-Alpaca-GPT4-From-OpenHermes-2.5 dataset:STEM-AI-mtl/Electrical-engineering dataset:knowrohit07/saraswati-stem dataset:sablo/oasst2_curated dataset:lmsys/lmsys-chat-1m dataset:TIGER-Lab/MathInstruct dataset:bigbio/med_qa dataset:meta-math/MetaMathQA-40K dataset:piqa dataset:scibench dataset:sciq dataset:Open-Orca/SlimOrca dataset:migtissera/Synthia-v1.3 dataset:allenai/WildChat dataset:microsoft/orca-math-word-problems-200k dataset:openchat/openchat_sharegpt4_dataset dataset:teknium/GPTeacher-General-Instruct dataset:m-a-p/CodeFeedback-Filtered-Instruction base_model:Weyaxi/Einstein-v5-v0.2-7B base_model:quantized:Weyaxi/Einstein-v5-v0.2-7B license:other endpoints_compatible region:us conversational
---
base_model: Weyaxi/Einstein-v5-v0.2-7B
datasets:
- allenai/ai2_arc
- camel-ai/physics
- camel-ai/chemistry
- camel-ai/biology
- camel-ai/math
- metaeval/reclor
- openbookqa
- mandyyyyii/scibench
- derek-thomas/ScienceQA
- TIGER-Lab/ScienceEval
- jondurbin/airoboros-3.2
- LDJnr/Capybara
- Cot-Alpaca-GPT4-From-OpenHermes-2.5
- STEM-AI-mtl/Electrical-engineering
- knowrohit07/saraswati-stem
- sablo/oasst2_curated
- lmsys/lmsys-chat-1m
- TIGER-Lab/MathInstruct
- bigbio/med_qa
- meta-math/MetaMathQA-40K
- openbookqa
- piqa
- metaeval/reclor
- derek-thomas/ScienceQA
- scibench
- sciq
- Open-Orca/SlimOrca
- migtissera/Synthia-v1.3
- TIGER-Lab/ScienceEval
- allenai/WildChat
- microsoft/orca-math-word-problems-200k
- openchat/openchat_sharegpt4_dataset
- teknium/GPTeacher-General-Instruct
- m-a-p/CodeFeedback-Filtered-Instruction
language:
- en
library_name: transformers
license: other
tags:
- axolotl
- generated_from_trainer
- Mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- science
- physics
- chemistry
- biology
- math
quantized_by: mradermacher
---
## About

<!-- ### quantize_version: 2 -->
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static quants of https://huggingface.co/Weyaxi/Einstein-v5-v0.2-7B

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-i1-GGUF
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.

## Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q2_K.gguf) | Q2_K | 2.8 |  |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 |  |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 |  |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 |  |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 |  |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 |  |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Einstein-v5-v0.2-7B-GGUF/resolve/main/Einstein-v5-v0.2-7B.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

## FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

## Thanks

I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

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