Instructions to use michaelfeil/ct2fast-mpt-30b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use michaelfeil/ct2fast-mpt-30b-chat with Transformers:

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

pipe = pipeline("text-generation", model="michaelfeil/ct2fast-mpt-30b-chat", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("michaelfeil/ct2fast-mpt-30b-chat", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("michaelfeil/ct2fast-mpt-30b-chat", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use michaelfeil/ct2fast-mpt-30b-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "michaelfeil/ct2fast-mpt-30b-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "michaelfeil/ct2fast-mpt-30b-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/michaelfeil/ct2fast-mpt-30b-chat

SGLang

How to use michaelfeil/ct2fast-mpt-30b-chat 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 "michaelfeil/ct2fast-mpt-30b-chat" \
    --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": "michaelfeil/ct2fast-mpt-30b-chat",
		"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 "michaelfeil/ct2fast-mpt-30b-chat" \
        --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": "michaelfeil/ct2fast-mpt-30b-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use michaelfeil/ct2fast-mpt-30b-chat with Docker Model Runner:
```
docker model run hf.co/michaelfeil/ct2fast-mpt-30b-chat
```

ct2fast-mpt-30b-chat / model.bin

michaelfeil

Upload mosaicml/mpt-30b-chat ctranslate fp16 weights

b2bfa61 almost 3 years ago

download

history blame contribute delete

30 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 16fb1d383002ef3215eb6d7580f8ea3d9269eb5f562e242e7d5e6943a0310221
Size of remote file:: 30 GB
SHA256:: c63c1de64f18b04783aed6c99e29328dea1c5998abf62195d4765c2efe70e494

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.