metadata
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
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)
Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server
hkust-nlp/dart-math-llama3-8b-prop2diff - GGUF
This repo contains GGUF format model files for hkust-nlp/dart-math-llama3-8b-prop2diff.
The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.
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Model file specification
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| 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 | Q3_K_S | 3.665 GB | very small, high quality loss |
| 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 | Q3_K_L | 4.322 GB | small, substantial quality loss |
| 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 | Q4_K_S | 4.693 GB | small, greater quality loss |
| 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 | Q5_0 | 5.600 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| 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 | Q5_K_M | 5.733 GB | large, very low quality loss - recommended |
| 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 | Q8_0 | 8.541 GB | very large, extremely low quality loss - not recommended |
Downloading instruction
Command line
Firstly, install Huggingface Client
pip install -U "huggingface_hub[cli]"
Then, downoad the individual model file the a local directory
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:
huggingface-cli download tensorblock/dart-math-llama3-8b-prop2diff-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'