Instructions to use tsunemoto/WizardMath-7B-V1.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tsunemoto/WizardMath-7B-V1.1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tsunemoto/WizardMath-7B-V1.1-GGUF", filename="wizardmath-7b-v1.1.Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use tsunemoto/WizardMath-7B-V1.1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use tsunemoto/WizardMath-7B-V1.1-GGUF with Ollama:
ollama run hf.co/tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M
- Unsloth Studio new
How to use tsunemoto/WizardMath-7B-V1.1-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tsunemoto/WizardMath-7B-V1.1-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tsunemoto/WizardMath-7B-V1.1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tsunemoto/WizardMath-7B-V1.1-GGUF to start chatting
- Docker Model Runner
How to use tsunemoto/WizardMath-7B-V1.1-GGUF with Docker Model Runner:
docker model run hf.co/tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M
- Lemonade
How to use tsunemoto/WizardMath-7B-V1.1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tsunemoto/WizardMath-7B-V1.1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.WizardMath-7B-V1.1-GGUF-Q4_K_M
List all available models
lemonade list
- Tsunemoto GGUF's of WizardMath-7B-V1.1
- Original Repo Link:
- Original Model Card:
- WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (RLEIF)
- News
- [12/19/2023] Comparing WizardMath-7B-V1.1 with other open source 7B size math LLMs.
- [12/19/2023] Comparing WizardMath-7B-V1.1 with large open source (30B~70B) LLMs.
- Inference WizardMath Demo Script
- Citation
- Original Repo Link:
Tsunemoto GGUF's of WizardMath-7B-V1.1
This is a GGUF quantization of WizardMath-7B-V1.1.
Original Repo Link:
Original Model Card:
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (RLEIF)
π Home Page
π€ HF Repo β’π± Github Repo β’ π¦ Twitter
π [WizardLM] β’ π [WizardCoder] β’ π [WizardMath]
π Join our Discord
News
[12/19/2023] π₯ We released WizardMath-7B-V1.1 trained from Mistral-7B, the SOTA 7B math LLM, achieves 83.2 pass@1 on GSM8k, and 33.0 pass@1 on MATH.
[12/19/2023] π₯ WizardMath-7B-V1.1 outperforms ChatGPT 3.5, Gemini Pro, Mixtral MOE, and Claude Instant on GSM8K pass@1.
[12/19/2023] π₯ WizardMath-7B-V1.1 is comparable with ChatGPT 3.5, Gemini Pro, and surpasses Mixtral MOE on MATH pass@1.
| Model | Checkpoint | Paper | GSM8k | MATH |
|---|---|---|---|---|
| WizardMath-7B-V1.1 | π€ HF Link | π [WizardMath] | 83.2 | 33.0 |
| WizardMath-70B-V1.0 | π€ HF Link | π [WizardMath] | 81.6 | 22.7 |
| WizardMath-13B-V1.0 | π€ HF Link | π [WizardMath] | 63.9 | 14.0 |
| WizardMath-7B-V1.0 | π€ HF Link | π [WizardMath] | 54.9 | 10.7 |
[12/19/2023] Comparing WizardMath-7B-V1.1 with other open source 7B size math LLMs.
| Model | GSM8k Pass@1 | MATH Pass@1 |
|---|---|---|
| MPT-7B | 6.8 | 3.0 |
| Llama 1-7B | 11.0 | 2.9 |
| Llama 2-7B | 12.3 | 2.8 |
| Yi-6b | 32.6 | 5.8 |
| Mistral-7B | 37.8 | 9.1 |
| Qwen-7b | 47.8 | 9.3 |
| RFT-7B | 50.3 | -- |
| MAmmoTH-7B (COT) | 50.5 | 10.4 |
| WizardMath-7B-V1.0 | 54.9 | 10.7 |
| Abel-7B-001 | 59.7 | 13 |
| MetaMath-7B | 66.5 | 19.8 |
| Arithmo-Mistral-7B | 74.7 | 25.3 |
| MetaMath-Mistral-7B | 77.7 | 28.2 |
| Abel-7B-002 | 80.4 | 29.5 |
| WizardMath-7B-V1.1 | 83.2 | 33.0 |
[12/19/2023] Comparing WizardMath-7B-V1.1 with large open source (30B~70B) LLMs.
| Model | GSM8k Pass@1 | MATH Pass@1 |
|---|---|---|
| Llemma-34B | 51.5 | 25.0 |
| Minerva-62B | 52.4 | 27.6 |
| Llama 2-70B | 56.8 | 13.5 |
| DeepSeek 67B | 63.4 | -- |
| Gork 33B | 62.9 | 23.9 |
| MAmmoTH-70B | 72.4 | 21.1 |
| Yi-34B | 67.9 | 15.9 |
| Mixtral 8x7B | 74.4 | 28.4 |
| MetaMath-70B | 82.3 | 26.6 |
| WizardMath-7B-V1.1 | 83.2 | 33.0 |
π₯ βNote for model system prompts usage:
Please use the same systems prompts strictly with us, and we do not guarantee the accuracy of the quantified versions.
Default version:
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
CoT Version: οΌβFor the simple math questions, we do NOT recommend to use the CoT prompt.οΌ
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response: Let's think step by step."
Inference WizardMath Demo Script
We provide the WizardMath inference demo code here.
Citation
Please cite the repo if you use the data, method or code in this repo.
@article{luo2023wizardmath,
title={WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct},
author={Luo, Haipeng and Sun, Qingfeng and Xu, Can and Zhao, Pu and Lou, Jianguang and Tao, Chongyang and Geng, Xiubo and Lin, Qingwei and Chen, Shifeng and Zhang, Dongmei},
journal={arXiv preprint arXiv:2308.09583},
year={2023}
}
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