---
language:
- en
license: other
library_name: transformers
datasets:
- Open-Orca/SlimOrca
- m-a-p/Code-Feedback
- MaziyarPanahi/WizardLM_evol_instruct_V2_196k
- camel-ai/math
- camel-ai/physics
- camel-ai/biology
- camel-ai/chemistry
- LDJnr/Capybara
- jondurbin/airoboros-3.2
- microsoft/orca-math-word-problems-200k
inference:
  parameters:
    do_sample: true
    temperature: 0.8
    top_p: 0.95
    top_k: 40
    max_new_tokens: 250
    repetition_penalty: 1.1
base_model: M4-ai/Orca-2.0-Tau-1.8B
tags:
- TensorBlock
- GGUF
model-index:
- name: Orca-2.0-Tau-1.8B
  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: 37.12
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
      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: 61.13
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
      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: 45.27
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
      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: 39.1
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
      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: 59.59
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
      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: 28.96
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
      name: Open LLM Leaderboard
---

<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
        </p>
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## M4-ai/Orca-2.0-Tau-1.8B - GGUF

This repo contains GGUF format model files for [M4-ai/Orca-2.0-Tau-1.8B](https://huggingface.co/M4-ai/Orca-2.0-Tau-1.8B).

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

```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Orca-2.0-Tau-1.8B-Q2_K.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q2_K.gguf) | Q2_K | 0.847 GB | smallest, significant quality loss - not recommended for most purposes |
| [Orca-2.0-Tau-1.8B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q3_K_S.gguf) | Q3_K_S | 0.954 GB | very small, high quality loss |
| [Orca-2.0-Tau-1.8B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q3_K_M.gguf) | Q3_K_M | 1.016 GB | very small, high quality loss |
| [Orca-2.0-Tau-1.8B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q3_K_L.gguf) | Q3_K_L | 1.056 GB | small, substantial quality loss |
| [Orca-2.0-Tau-1.8B-Q4_0.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q4_0.gguf) | Q4_0 | 1.120 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Orca-2.0-Tau-1.8B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q4_K_S.gguf) | Q4_K_S | 1.158 GB | small, greater quality loss |
| [Orca-2.0-Tau-1.8B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q4_K_M.gguf) | Q4_K_M | 1.218 GB | medium, balanced quality - recommended |
| [Orca-2.0-Tau-1.8B-Q5_0.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q5_0.gguf) | Q5_0 | 1.311 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Orca-2.0-Tau-1.8B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q5_K_S.gguf) | Q5_K_S | 1.328 GB | large, low quality loss - recommended |
| [Orca-2.0-Tau-1.8B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q5_K_M.gguf) | Q5_K_M | 1.377 GB | large, very low quality loss - recommended |
| [Orca-2.0-Tau-1.8B-Q6_K.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q6_K.gguf) | Q6_K | 1.579 GB | very large, extremely low quality loss |
| [Orca-2.0-Tau-1.8B-Q8_0.gguf](https://huggingface.co/tensorblock/Orca-2.0-Tau-1.8B-GGUF/blob/main/Orca-2.0-Tau-1.8B-Q8_0.gguf) | Q8_0 | 1.958 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/Orca-2.0-Tau-1.8B-GGUF --include "Orca-2.0-Tau-1.8B-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/Orca-2.0-Tau-1.8B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```