---
license: llama3
library_name: transformers
datasets:
- aqua_rat
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
base_model: abacusai/Smaug-Llama-3-70B-Instruct-32K
tags:
- TensorBlock
- GGUF
model-index:
- name: Smaug-Llama-3-70B-Instruct-32K
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: HuggingFaceH4/ifeval
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 77.61
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct-32K
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: BBH
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 49.07
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct-32K
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: hendrycks/competition_math
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 21.22
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct-32K
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.15
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct-32K
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 12.43
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct-32K
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 41.83
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=abacusai/Smaug-Llama-3-70B-Instruct-32K
      name: Open LLM Leaderboard
---

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## abacusai/Smaug-Llama-3-70B-Instruct-32K - GGUF

This repo contains GGUF format model files for [abacusai/Smaug-Llama-3-70B-Instruct-32K](https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct-32K).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```
<|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|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Smaug-Llama-3-70B-Instruct-32K-Q2_K.gguf](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q2_K.gguf) | Q2_K | 26.375 GB | smallest, significant quality loss - not recommended for most purposes |
| [Smaug-Llama-3-70B-Instruct-32K-Q3_K_S.gguf](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q3_K_S.gguf) | Q3_K_S | 30.912 GB | very small, high quality loss |
| [Smaug-Llama-3-70B-Instruct-32K-Q3_K_M.gguf](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q3_K_M.gguf) | Q3_K_M | 34.267 GB | very small, high quality loss |
| [Smaug-Llama-3-70B-Instruct-32K-Q3_K_L.gguf](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q3_K_L.gguf) | Q3_K_L | 37.141 GB | small, substantial quality loss |
| [Smaug-Llama-3-70B-Instruct-32K-Q4_0.gguf](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q4_0.gguf) | Q4_0 | 39.970 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Smaug-Llama-3-70B-Instruct-32K-Q4_K_S.gguf](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q4_K_S.gguf) | Q4_K_S | 40.347 GB | small, greater quality loss |
| [Smaug-Llama-3-70B-Instruct-32K-Q4_K_M.gguf](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q4_K_M.gguf) | Q4_K_M | 42.520 GB | medium, balanced quality - recommended |
| [Smaug-Llama-3-70B-Instruct-32K-Q5_0.gguf](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q5_0.gguf) | Q5_0 | 48.657 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Smaug-Llama-3-70B-Instruct-32K-Q5_K_S.gguf](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q5_K_S.gguf) | Q5_K_S | 48.657 GB | large, low quality loss - recommended |
| [Smaug-Llama-3-70B-Instruct-32K-Q5_K_M.gguf](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q5_K_M.gguf) | Q5_K_M | 49.950 GB | large, very low quality loss - recommended |
| [Smaug-Llama-3-70B-Instruct-32K-Q8_0](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q8_0) | Q6_K | 74.975 GB | very large, extremely low quality loss |
| [Smaug-Llama-3-70B-Instruct-32K-Q6_K](https://huggingface.co/tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF/blob/main/Smaug-Llama-3-70B-Instruct-32K-Q6_K) | Q8_0 | 57.888 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF --include "Smaug-Llama-3-70B-Instruct-32K-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/Smaug-Llama-3-70B-Instruct-32K-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```