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Llamacpp quants
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metadata
language:
  - en
license: other
tags:
  - axolotl
  - generated_from_trainer
  - instruct
  - finetune
  - chatml
  - gpt4
  - synthetic data
  - science
  - physics
  - chemistry
  - biology
  - math
  - llama
  - llama3
base_model: meta-llama/Meta-Llama-3-8B
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
model-index:
  - name: Einstein-v6.1-Llama3-8B
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 62.46
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 82.41
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 66.19
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 55.1
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 79.32
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 66.11
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
          name: Open LLM Leaderboard
quantized_by: bartowski
pipeline_tag: text-generation

Llamacpp imatrix Quantizations of Einstein-v6.1-Llama3-8B

Using llama.cpp release b2777 for quantization.

Original model: https://huggingface.co/Weyaxi/Einstein-v6.1-Llama3-8B

All quants made using imatrix option with dataset provided by Kalomaze here

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Download a file (not the whole branch) from below:

Filename Quant type File Size Description
Einstein-v6.1-Llama3-8B-Q8_0.gguf Q8_0 8.54GB Extremely high quality, generally unneeded but max available quant.
Einstein-v6.1-Llama3-8B-Q6_K.gguf Q6_K 6.59GB Very high quality, near perfect, recommended.
Einstein-v6.1-Llama3-8B-Q5_K_M.gguf Q5_K_M 5.73GB High quality, recommended.
Einstein-v6.1-Llama3-8B-Q5_K_S.gguf Q5_K_S 5.59GB High quality, recommended.
Einstein-v6.1-Llama3-8B-Q4_K_M.gguf Q4_K_M 4.92GB Good quality, uses about 4.83 bits per weight, recommended.
Einstein-v6.1-Llama3-8B-Q4_K_S.gguf Q4_K_S 4.69GB Slightly lower quality with more space savings, recommended.
Einstein-v6.1-Llama3-8B-IQ4_NL.gguf IQ4_NL 4.67GB Decent quality, slightly smaller than Q4_K_S with similar performance recommended.
Einstein-v6.1-Llama3-8B-IQ4_XS.gguf IQ4_XS 4.44GB Decent quality, smaller than Q4_K_S with similar performance, recommended.
Einstein-v6.1-Llama3-8B-Q3_K_L.gguf Q3_K_L 4.32GB Lower quality but usable, good for low RAM availability.
Einstein-v6.1-Llama3-8B-Q3_K_M.gguf Q3_K_M 4.01GB Even lower quality.
Einstein-v6.1-Llama3-8B-IQ3_M.gguf IQ3_M 3.78GB Medium-low quality, new method with decent performance comparable to Q3_K_M.
Einstein-v6.1-Llama3-8B-IQ3_S.gguf IQ3_S 3.68GB Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance.
Einstein-v6.1-Llama3-8B-Q3_K_S.gguf Q3_K_S 3.66GB Low quality, not recommended.
Einstein-v6.1-Llama3-8B-IQ3_XS.gguf IQ3_XS 3.51GB Lower quality, new method with decent performance, slightly better than Q3_K_S.
Einstein-v6.1-Llama3-8B-IQ3_XXS.gguf IQ3_XXS 3.27GB Lower quality, new method with decent performance, comparable to Q3 quants.
Einstein-v6.1-Llama3-8B-Q2_K.gguf Q2_K 3.17GB Very low quality but surprisingly usable.
Einstein-v6.1-Llama3-8B-IQ2_M.gguf IQ2_M 2.94GB Very low quality, uses SOTA techniques to also be surprisingly usable.
Einstein-v6.1-Llama3-8B-IQ2_S.gguf IQ2_S 2.75GB Very low quality, uses SOTA techniques to be usable.
Einstein-v6.1-Llama3-8B-IQ2_XS.gguf IQ2_XS 2.60GB Very low quality, uses SOTA techniques to be usable.
Einstein-v6.1-Llama3-8B-IQ2_XXS.gguf IQ2_XXS 2.39GB Lower quality, uses SOTA techniques to be usable.
Einstein-v6.1-Llama3-8B-IQ1_M.gguf IQ1_M 2.16GB Extremely low quality, not recommended.
Einstein-v6.1-Llama3-8B-IQ1_S.gguf IQ1_S 2.01GB Extremely low quality, not recommended.

Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download bartowski/Einstein-v6.1-Llama3-8B-GGUF --include "Einstein-v6.1-Llama3-8B-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/Einstein-v6.1-Llama3-8B-GGUF --include "Einstein-v6.1-Llama3-8B-Q8_0.gguf/*" --local-dir Einstein-v6.1-Llama3-8B-Q8_0 --local-dir-use-symlinks False

You can either specify a new local-dir (Einstein-v6.1-Llama3-8B-Q8_0) or download them all in place (./)

Which file should I choose?

A great write up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski