Instructions to use fdqerq22ds/MathScale-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fdqerq22ds/MathScale-Mistral with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="fdqerq22ds/MathScale-Mistral")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fdqerq22ds/MathScale-Mistral")
model = AutoModelForCausalLM.from_pretrained("fdqerq22ds/MathScale-Mistral")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use fdqerq22ds/MathScale-Mistral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fdqerq22ds/MathScale-Mistral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fdqerq22ds/MathScale-Mistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fdqerq22ds/MathScale-Mistral

SGLang

How to use fdqerq22ds/MathScale-Mistral with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "fdqerq22ds/MathScale-Mistral" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fdqerq22ds/MathScale-Mistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "fdqerq22ds/MathScale-Mistral" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fdqerq22ds/MathScale-Mistral",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fdqerq22ds/MathScale-Mistral with Docker Model Runner:
```
docker model run hf.co/fdqerq22ds/MathScale-Mistral
```

fdqerq22ds commited on Jun 14, 2024

Commit

afde5d4

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1 Parent(s): e858e51

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@@ -1,3 +1,15 @@
----
-license: cc-by-nc-4.0
----

+---
+license: cc-by-nc-4.0
+datasets:
+- fdqerq22ds/MathScaleQA-2M
+---
+## Overview
+This is a reproduced MathScale-Mistral model by finetuning the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on our reproduced [MathScaleQA-2M](https://huggingface.co/datasets/fdqerq22ds/MathScaleQA-2M) dataset, following the hyperparameters in their [original paper](https://arxiv.org/abs/2403.02884) to ensure the effectiveness of our reproduction.
+## Reproduction Details
+Fortunately, the reproduction was smooth, and we managed to match the reported performance metrics when evaluating on their [MWPBench](https://github.com/microsoft/unilm/tree/master/mathscale/MWPBench). Below, we present a comparison between the performance of their official model and our reproduced model:
+| Model                         | GSM8K | MATH | CollegeMath | TAL  | Math23k | Ape210k | GaokaoBench-Math | AGIE-Gaokao-Math | AGIE-SAT-Math | AGIE-MATH | MicroAverage | MacroAverage |
+|-------------------------------|-------|------|-------------|------|---------|---------|------------------|------------------|---------------|-----------|--------------|--------------|
+| Official MathScale-Mistral    | 74.8  | 35.2 | 21.8        | 39.9 | 64.4    | 46.0    | 21.4             | 14.3             | 57.8          | 32.9      | 38.7         | 40.8         |
+| [Reproduced MathScale-Mistral](https://huggingface.co/fdqerq22ds/MathScale-Mistral)  | 74.0  | 34.5 | 22.0        | 39.6 | 61.7    | 45.1    | 21.6             | 15.5             | 56.8          | 34.4      | 38.3         | 40.5         |