--- base_model: Weyaxi/Einstein-v7-Qwen2-7B 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 - totally-not-an-llm/EverythingLM-data-V3 - HuggingFaceH4/no_robots - OpenAssistant/oasst_top1_2023-08-25 - WizardLM/WizardLM_evol_instruct_70k - abacusai/SystemChat-1.1 - H-D-T/Buzz-V1.2 language: - en library_name: transformers license: other quantized_by: mradermacher tags: - axolotl - instruct - finetune - chatml - gpt4 - synthetic data - science - physics - chemistry - biology - math - qwen - qwen2 --- ## About static quants of https://huggingface.co/Weyaxi/Einstein-v7-Qwen2-7B weighted/imatrix quants are available at https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-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-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q2_K.gguf) | Q2_K | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.IQ3_XS.gguf) | IQ3_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.IQ3_S.gguf) | IQ3_S | 3.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.IQ3_M.gguf) | IQ3_M | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Einstein-v7-Qwen2-7B-GGUF/resolve/main/Einstein-v7-Qwen2-7B.f16.gguf) | f16 | 15.3 | 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.