README.md · bartowski/Einstein-v6.1-Llama3-8B-GGUF at main

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