Instructions to use RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds

SGLang

How to use RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds 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 "RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds" \
    --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": "RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds",
		"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 "RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds" \
        --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": "RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds with Docker Model Runner:
```
docker model run hf.co/RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds
```

mpt-7b-gsm8k-pruned75-quant

Paper: Sparse Finetuning for Inference Acceleration of Large Language Models
Code: https://github.com/neuralmagic/deepsparse/tree/main/research/mpt

This model was produced from a MPT-7B base model finetuned on the GSM8k dataset with pruning applied using SparseGPT and retrain for 4 epochs with L2 distillation. Then it was exported for optimized inference with DeepSparse.

GSM8k zero-shot accuracy with lm-evaluation-harness : 26.61% (FP32 baseline is 28.2%)

Usage

from deepsparse import TextGeneration
model_path = "hf:neuralmagic/mpt-7b-gsm8k-pruned75-quant" # or use a sparsezoo stub (zoo:mpt-7b-gsm8k_mpt_pretrain-pruned75_quantized)
model = TextGeneration(model=model_path)
model("There are twice as many boys as girls at Dr. Wertz's school. If there are 60 girls and 5 students to every teacher, how many teachers are there?", max_new_tokens=50)

All MPT model weights are available on SparseZoo and CPU speedup for generative inference can be reproduced by following the instructions at DeepSparse

Model Links	Compression
neuralmagic/mpt-7b-gsm8k-quant	Quantization (W8A8)
neuralmagic/mpt-7b-gsm8k-pruned40-quant	Quantization (W8A8) & 40% Pruning
neuralmagic/mpt-7b-gsm8k-pruned50-quant	Quantization (W8A8) & 50% Pruning
neuralmagic/mpt-7b-gsm8k-pruned60-quant	Quantization (W8A8) & 60% Pruning
neuralmagic/mpt-7b-gsm8k-pruned70-quant	Quantization (W8A8) & 70% Pruning
neuralmagic/mpt-7b-gsm8k-pruned70-quant	Quantization (W8A8) & 75% Pruning
neuralmagic/mpt-7b-gsm8k-pruned80-quant	Quantization (W8A8) & 80% Pruning

For general questions on these models and sparsification methods, reach out to the engineering team on our community Slack.

Downloads last month: 6

Dataset used to train RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds

Papers for RedHatAI/mpt-7b-gsm8k-pruned75-quant-ds

Sparse Finetuning for Inference Acceleration of Large Language Models

Paper • 2310.06927 • Published Oct 10, 2023 • 15

SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Paper • 2301.00774 • Published Jan 2, 2023 • 4