---
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: .64683835842678326
      verified: True
  - 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: 
      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: 
      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: 
      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: 
    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: 
      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: 
      name: accuracy
    source:
      url: https://www.placeholderurl.com
      name: Open LLM Leaderboard
quantized_by: bartowski
pipeline_tag: text-generation
---

## Llamacpp imatrix Quantizations of Llama3-8B-Instruct-Replete-Adapted

Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3291">b3291</a> for quantization.

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

All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)

## Prompt format

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>


```

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

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama3-8B-Instruct-Replete-Adapted-Q8_0.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. |
| [Llama3-8B-Instruct-Replete-Adapted-Q6_K_L.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q6_K_L.gguf) | Q6_K_L | 6.85GB | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Llama3-8B-Instruct-Replete-Adapted-Q6_K.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. |
| [Llama3-8B-Instruct-Replete-Adapted-Q5_K_L.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q5_K_L.gguf) | Q5_K_L | 6.05GB | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Llama3-8B-Instruct-Replete-Adapted-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. |
| [Llama3-8B-Instruct-Replete-Adapted-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. |
| [Llama3-8B-Instruct-Replete-Adapted-Q4_K_L.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q4_K_L.gguf) | Q4_K_L | 5.31GB | Uses Q8_0 for embed and output weights. Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Llama3-8B-Instruct-Replete-Adapted-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Llama3-8B-Instruct-Replete-Adapted-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. |
| [Llama3-8B-Instruct-Replete-Adapted-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Llama3-8B-Instruct-Replete-Adapted-Q3_K_XL.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q3_K_XL.gguf) | Q3_K_XL | 4.78GB | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Llama3-8B-Instruct-Replete-Adapted-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. |
| [Llama3-8B-Instruct-Replete-Adapted-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. |
| [Llama3-8B-Instruct-Replete-Adapted-IQ3_M.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Llama3-8B-Instruct-Replete-Adapted-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. |
| [Llama3-8B-Instruct-Replete-Adapted-IQ3_XS.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Llama3-8B-Instruct-Replete-Adapted-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Llama3-8B-Instruct-Replete-Adapted-Q2_K_L.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q2_K_L.gguf) | Q2_K_L | 3.69GB | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Llama3-8B-Instruct-Replete-Adapted-Q2_K.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. |
| [Llama3-8B-Instruct-Replete-Adapted-IQ2_M.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Llama3-8B-Instruct-Replete-Adapted-IQ2_S.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. |
| [Llama3-8B-Instruct-Replete-Adapted-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama3-8B-Instruct-Replete-Adapted-GGUF/blob/main/Llama3-8B-Instruct-Replete-Adapted-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. |

## Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset

Thank you ZeroWw for the inspiration to experiment with embed/output

## 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/Llama3-8B-Instruct-Replete-Adapted-GGUF --include "Llama3-8B-Instruct-Replete-Adapted-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/Llama3-8B-Instruct-Replete-Adapted-GGUF --include "Llama3-8B-Instruct-Replete-Adapted-Q8_0.gguf/*" --local-dir Llama3-8B-Instruct-Replete-Adapted-Q8_0
```

You can either specify a new local-dir (Llama3-8B-Instruct-Replete-Adapted-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](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)

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](https://github.com/ggerganov/llama.cpp/wiki/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