Instructions to use TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge
- SGLang
How to use TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge 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 "TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge" \ --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": "TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge", "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 "TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge" \ --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": "TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge with Docker Model Runner:
docker model run hf.co/TehVenom/MPT-7b-Chat-Instruct-LongCTX-Merge
The specific prompting is not unknown
#3
by 2EyeGuy - opened
This is the specific prompting format for MPT-7b-Chat (60% of the model):
<|im_start|>system
- You are a helpful assistant chatbot trained by MosaicML.
- You answer questions.
- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
<|im_start|>user
How are you<|im_end|>
<|im_start|>assistant
I am doing well!<|im_end|>
<|im_start|>user
How are you now?<|im_end|>
But it's unknown whether there should be a \n between <|im_end|> and <|im_start|>.
source:
- https://huggingface.co/spaces/mosaicml/mpt-7b-chat/blob/main/app.py
- https://github.com/openai/openai-python/blob/main/chatml.md
The specific prompting for MPT-7b-Instruct (20% of the model) is the same as Alpaca.
But make sure your tokeniser recognises those tokens.