README.md · bartowski/Replete-Coder-Llama3-8B-GGUF at main

metadata

license: other
license_name: llama-3
license_link: https://llama.meta.com/llama3/license/
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
datasets:
  - Replete-AI/code_bagel_hermes-2.5
  - Replete-AI/code_bagel
  - Replete-AI/OpenHermes-2.5-Uncensored
  - teknium/OpenHermes-2.5
  - layoric/tiny-codes-alpaca
  - glaiveai/glaive-code-assistant-v3
  - ajibawa-2023/Code-290k-ShareGPT
  - TIGER-Lab/MathInstruct
  - chargoddard/commitpack-ft-instruct-rated
  - iamturun/code_instructions_120k_alpaca
  - ise-uiuc/Magicoder-Evol-Instruct-110K
  - cognitivecomputations/dolphin-coder
  - nickrosh/Evol-Instruct-Code-80k-v1
  - coseal/CodeUltraFeedback_binarized
  - glaiveai/glaive-function-calling-v2
  - CyberNative/Code_Vulnerability_Security_DPO
  - jondurbin/airoboros-2.2
  - camel-ai
  - lmsys/lmsys-chat-1m
  - CollectiveCognition/chats-data-2023-09-22
  - CoT-Alpaca-GPT4
  - WizardLM/WizardLM_evol_instruct_70k
  - WizardLM/WizardLM_evol_instruct_V2_196k
  - teknium/GPT4-LLM-Cleaned
  - GPTeacher
  - OpenGPT
  - meta-math/MetaMathQA
  - Open-Orca/SlimOrca
  - garage-bAInd/Open-Platypus
  - anon8231489123/ShareGPT_Vicuna_unfiltered
  - Unnatural-Instructions-GPT4
model-index:
  - name: Replete-Coder-llama3-8b
    results:
      - task:
          name: HumanEval
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            verified: false
      - task:
          name: AI2 Reasoning Challenge
          type: text-generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: accuracy
            value: null
            name: normalized accuracy
        source:
          url: https://www.placeholderurl.com
          name: Open LLM Leaderboard
      - task:
          name: Text Generation
          type: text-generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: accuracy
            value: null
            name: normalized accuracy
        source:
          url: https://www.placeholderurl.com
          name: Open LLM Leaderboard
      - task:
          name: Text Generation
          type: text-generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: accuracy
            value: null
            name: accuracy
        source:
          url: https://www.placeholderurl.com
          name: Open LLM Leaderboard
      - task:
          name: Text Generation
          type: text-generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: multiple_choice_accuracy
            value: null
        source:
          url: https://www.placeholderurl.com
          name: Open LLM Leaderboard
      - task:
          name: Text Generation
          type: text-generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: accuracy
            value: null
            name: accuracy
        source:
          url: https://www.placeholderurl.com
          name: Open LLM Leaderboard
      - task:
          name: Text Generation
          type: text-generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: accuracy
            value: null
            name: accuracy
        source:
          url: https://www.placeholderurl.com
          name: Open LLM Leaderboard
quantized_by: bartowski
pipeline_tag: text-generation

Llamacpp imatrix Quantizations of Replete-Coder-Llama3-8B

Using llama.cpp release b3197 for quantization.

Original model: https://huggingface.co/Replete-AI/Replete-Coder-Llama3-8B

All quants made using imatrix option with dataset from here

Prompt format

### System:
{}

### Instruction:
{}

### Response:
{}

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

Filename	Quant type	File Size	Description
Replete-Coder-Llama3-8B-Q8_0_L.gguf	Q8_0_L	9.52GB	Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant.
Replete-Coder-Llama3-8B-Q8_0.gguf	Q8_0	8.54GB	Extremely high quality, generally unneeded but max available quant.
Replete-Coder-Llama3-8B-Q6_K_L.gguf	Q6_K_L	7.83GB	Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, recommended.
Replete-Coder-Llama3-8B-Q6_K.gguf	Q6_K	6.59GB	Very high quality, near perfect, recommended.
Replete-Coder-Llama3-8B-Q5_K_L.gguf	Q5_K_L	7.04GB	Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, recommended.
Replete-Coder-Llama3-8B-Q5_K_M.gguf	Q5_K_M	5.73GB	High quality, recommended.
Replete-Coder-Llama3-8B-Q5_K_S.gguf	Q5_K_S	5.59GB	High quality, recommended.
Replete-Coder-Llama3-8B-Q4_K_L.gguf	Q4_K_L	6.29GB	Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, recommended.
Replete-Coder-Llama3-8B-Q4_K_M.gguf	Q4_K_M	4.92GB	Good quality, uses about 4.83 bits per weight, recommended.
Replete-Coder-Llama3-8B-Q4_K_S.gguf	Q4_K_S	4.69GB	Slightly lower quality with more space savings, recommended.
Replete-Coder-Llama3-8B-IQ4_XS.gguf	IQ4_XS	4.44GB	Decent quality, smaller than Q4_K_S with similar performance, recommended.
Replete-Coder-Llama3-8B-Q3_K_XL.gguf	Q3_K_XL		Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability.
Replete-Coder-Llama3-8B-Q3_K_L.gguf	Q3_K_L	4.32GB	Lower quality but usable, good for low RAM availability.
Replete-Coder-Llama3-8B-Q3_K_M.gguf	Q3_K_M	4.01GB	Even lower quality.
Replete-Coder-Llama3-8B-IQ3_M.gguf	IQ3_M	3.78GB	Medium-low quality, new method with decent performance comparable to Q3_K_M.
Replete-Coder-Llama3-8B-Q3_K_S.gguf	Q3_K_S	3.66GB	Low quality, not recommended.
Replete-Coder-Llama3-8B-IQ3_XS.gguf	IQ3_XS	3.51GB	Lower quality, new method with decent performance, slightly better than Q3_K_S.
Replete-Coder-Llama3-8B-IQ3_XXS.gguf	IQ3_XXS	3.27GB	Lower quality, new method with decent performance, comparable to Q3 quants.
Replete-Coder-Llama3-8B-Q2_K.gguf	Q2_K	3.17GB	Very low quality but surprisingly usable.
Replete-Coder-Llama3-8B-IQ2_M.gguf	IQ2_M	2.94GB	Very low quality, uses SOTA techniques to also be surprisingly usable.
Replete-Coder-Llama3-8B-IQ2_S.gguf	IQ2_S	2.75GB	Very low quality, uses SOTA techniques to be usable.
Replete-Coder-Llama3-8B-IQ2_XS.gguf	IQ2_XS	2.60GB	Very low quality, uses SOTA techniques to be usable.

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/Replete-Coder-Llama3-8B-GGUF --include "Replete-Coder-Llama3-8B-Q4_K_M.gguf" --local-dir ./

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/Replete-Coder-Llama3-8B-GGUF --include "Replete-Coder-Llama3-8B-Q8_0.gguf/*" --local-dir Replete-Coder-Llama3-8B-Q8_0

You can either specify a new local-dir (Replete-Coder-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