File size: 8,722 Bytes
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language:
- en
license: llama3
library_name: transformers
tags:
- mathematics
- TensorBlock
- GGUF
datasets:
- hkust-nlp/dart-math-hard
metrics:
- accuracy
pipeline_tag: text-generation
base_model: hkust-nlp/dart-math-llama3-8b-prop2diff
model-index:
- name: dart-math-llama3-8b-prop2diff
results:
- task:
type: text-generation
name: Mathematical Problem-Solving
dataset:
name: MATH
type: hendrycks/competition_math
split: test
metrics:
- type: accuracy
value: 46.6
name: Pass@1 (0-shot CoT)
- task:
type: text-generation
name: Mathematical Problem-Solving
dataset:
name: GSM8K
type: openai/gsm8k
config: main
split: test
metrics:
- type: accuracy
value: 81.1
name: Pass@1 (0-shot CoT)
- task:
type: text-generation
name: Mathematical Problem-Solving
dataset:
name: CollegeMath
type: college-math
metrics:
- type: accuracy
value: 28.8
name: Pass@1 (0-shot CoT)
- task:
type: text-generation
name: Mathematical Problem-Solving
dataset:
name: DeepMind-Mathematics
type: deepmind-mathematics
metrics:
- type: accuracy
value: 48.0
name: Pass@1 (0-shot CoT)
- task:
type: text-generation
name: Mathematical Problem-Solving
dataset:
name: OlympiadBench-OE_TO_maths_en_COMP
type: Hothan/OlympiadBench
config: OE_TO_maths_en_COMP
split: train
metrics:
- type: accuracy
value: 14.5
name: Pass@1 (0-shot CoT)
- task:
type: text-generation
name: Mathematical Problem-Solving
dataset:
name: TheoremQA
type: TIGER-Lab/TheoremQA
split: test
metrics:
- type: accuracy
value: 19.4
name: Pass@1 (0-shot CoT)
---
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## hkust-nlp/dart-math-llama3-8b-prop2diff - GGUF
This repo contains GGUF format model files for [hkust-nlp/dart-math-llama3-8b-prop2diff](https://huggingface.co/hkust-nlp/dart-math-llama3-8b-prop2diff).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">🚀 Try it now! 🚀</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
</tr>
</table>
## Prompt template
```
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [dart-math-llama3-8b-prop2diff-Q2_K.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q2_K.gguf) | Q2_K | 3.179 GB | smallest, significant quality loss - not recommended for most purposes |
| [dart-math-llama3-8b-prop2diff-Q3_K_S.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q3_K_S.gguf) | Q3_K_S | 3.665 GB | very small, high quality loss |
| [dart-math-llama3-8b-prop2diff-Q3_K_M.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q3_K_M.gguf) | Q3_K_M | 4.019 GB | very small, high quality loss |
| [dart-math-llama3-8b-prop2diff-Q3_K_L.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q3_K_L.gguf) | Q3_K_L | 4.322 GB | small, substantial quality loss |
| [dart-math-llama3-8b-prop2diff-Q4_0.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q4_0.gguf) | Q4_0 | 4.662 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [dart-math-llama3-8b-prop2diff-Q4_K_S.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q4_K_S.gguf) | Q4_K_S | 4.693 GB | small, greater quality loss |
| [dart-math-llama3-8b-prop2diff-Q4_K_M.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q4_K_M.gguf) | Q4_K_M | 4.921 GB | medium, balanced quality - recommended |
| [dart-math-llama3-8b-prop2diff-Q5_0.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q5_0.gguf) | Q5_0 | 5.600 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [dart-math-llama3-8b-prop2diff-Q5_K_S.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q5_K_S.gguf) | Q5_K_S | 5.600 GB | large, low quality loss - recommended |
| [dart-math-llama3-8b-prop2diff-Q5_K_M.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q5_K_M.gguf) | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
| [dart-math-llama3-8b-prop2diff-Q6_K.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q6_K.gguf) | Q6_K | 6.596 GB | very large, extremely low quality loss |
| [dart-math-llama3-8b-prop2diff-Q8_0.gguf](https://huggingface.co/tensorblock/dart-math-llama3-8b-prop2diff-GGUF/blob/main/dart-math-llama3-8b-prop2diff-Q8_0.gguf) | Q8_0 | 8.541 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/dart-math-llama3-8b-prop2diff-GGUF --include "dart-math-llama3-8b-prop2diff-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/dart-math-llama3-8b-prop2diff-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|