README.md

---
language:
- en
license: mit
pipeline_tag: text-generation
base_model: TomGrc/FusionNet_passthrough
tags:
- TensorBlock
- GGUF
model-index:
- name: FusionNet_passthrough
  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: 69.45
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_passthrough
      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: 87.72
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_passthrough
      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: 65.28
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_passthrough
      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: 67.65
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_passthrough
      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: 81.29
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_passthrough
      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: 24.26
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_passthrough
      name: Open LLM Leaderboard
---

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## TomGrc/FusionNet_passthrough - GGUF

This repo contains GGUF format model files for [TomGrc/FusionNet_passthrough](https://huggingface.co/TomGrc/FusionNet_passthrough).

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).

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## Prompt template

```
### System:
{system_prompt}

### User:
{prompt}

### Assistant:
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [FusionNet_passthrough-Q2_K.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q2_K.gguf) | Q2_K | 7.855 GB | smallest, significant quality loss - not recommended for most purposes |
| [FusionNet_passthrough-Q3_K_S.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q3_K_S.gguf) | Q3_K_S | 9.165 GB | very small, high quality loss |
| [FusionNet_passthrough-Q3_K_M.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q3_K_M.gguf) | Q3_K_M | 10.226 GB | very small, high quality loss |
| [FusionNet_passthrough-Q3_K_L.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q3_K_L.gguf) | Q3_K_L | 11.137 GB | small, substantial quality loss |
| [FusionNet_passthrough-Q4_0.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q4_0.gguf) | Q4_0 | 11.963 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [FusionNet_passthrough-Q4_K_S.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q4_K_S.gguf) | Q4_K_S | 12.053 GB | small, greater quality loss |
| [FusionNet_passthrough-Q4_K_M.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q4_K_M.gguf) | Q4_K_M | 12.741 GB | medium, balanced quality - recommended |
| [FusionNet_passthrough-Q5_0.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q5_0.gguf) | Q5_0 | 14.596 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [FusionNet_passthrough-Q5_K_S.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q5_K_S.gguf) | Q5_K_S | 14.596 GB | large, low quality loss - recommended |
| [FusionNet_passthrough-Q5_K_M.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q5_K_M.gguf) | Q5_K_M | 14.997 GB | large, very low quality loss - recommended |
| [FusionNet_passthrough-Q6_K.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q6_K.gguf) | Q6_K | 17.395 GB | very large, extremely low quality loss |
| [FusionNet_passthrough-Q8_0.gguf](https://huggingface.co/tensorblock/FusionNet_passthrough-GGUF/blob/main/FusionNet_passthrough-Q8_0.gguf) | Q8_0 | 22.529 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/FusionNet_passthrough-GGUF --include "FusionNet_passthrough-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/FusionNet_passthrough-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```