Instructions to use wtang06/mpt-125m-c4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wtang06/mpt-125m-c4 with Transformers:

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

pipe = pipeline("text-generation", model="wtang06/mpt-125m-c4", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wtang06/mpt-125m-c4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("wtang06/mpt-125m-c4", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use wtang06/mpt-125m-c4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wtang06/mpt-125m-c4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wtang06/mpt-125m-c4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/wtang06/mpt-125m-c4

SGLang

How to use wtang06/mpt-125m-c4 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 "wtang06/mpt-125m-c4" \
    --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": "wtang06/mpt-125m-c4",
		"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 "wtang06/mpt-125m-c4" \
        --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": "wtang06/mpt-125m-c4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use wtang06/mpt-125m-c4 with Docker Model Runner:
```
docker model run hf.co/wtang06/mpt-125m-c4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

mpt-125m-c4

Model Description

Pretrained model for MPT-125M trained on C4 dataset

Training data

Trained on HuggingFace C4 dataset

Training procedure

This model was trained on C4 for ~2.5B tokens. Training time was ~1 hour with 104 A100-40gb GPUs.

Intended Use and Limitations

This model is primarily for generating texts from a prompt. The purpose is to explore pretraining models for research.

Downloads last month: 1,779

Model tree for wtang06/mpt-125m-c4

Quantizations

2 models

wtang06
/

mpt-125m-c4