Instructions to use Multi-Domain-Expert-Learning/given-mpt-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Multi-Domain-Expert-Learning/given-mpt-7b with Transformers:

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

pipe = pipeline("text-generation", model="Multi-Domain-Expert-Learning/given-mpt-7b", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Multi-Domain-Expert-Learning/given-mpt-7b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Multi-Domain-Expert-Learning/given-mpt-7b", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Multi-Domain-Expert-Learning/given-mpt-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Multi-Domain-Expert-Learning/given-mpt-7b

SGLang

How to use Multi-Domain-Expert-Learning/given-mpt-7b 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 "Multi-Domain-Expert-Learning/given-mpt-7b" \
    --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": "Multi-Domain-Expert-Learning/given-mpt-7b",
		"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 "Multi-Domain-Expert-Learning/given-mpt-7b" \
        --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": "Multi-Domain-Expert-Learning/given-mpt-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Multi-Domain-Expert-Learning/given-mpt-7b with Docker Model Runner:
```
docker model run hf.co/Multi-Domain-Expert-Learning/given-mpt-7b
```

Ontocord.AI commited on Jun 27, 2023

Commit

3a6a19a

1 Parent(s): c5e4b95

Upload modeling_mpt.py with huggingface_hub

Browse files

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modeling_mpt.py +2 -0

modeling_mpt.py CHANGED Viewed

@@ -1,4 +1,5 @@
 """A simple, flexible implementation of a GPT model.
 Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
 """
 import math
@@ -307,6 +308,7 @@ class MPTForCausalLM(MPTPreTrainedModel):
     @staticmethod
     def _reorder_cache(past_key_values, beam_idx):
         """Used by HuggingFace generate when using beam search with kv-caching.
         See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
         for an example in transformers.
         """

 """A simple, flexible implementation of a GPT model.
 Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
 """
 import math
     @staticmethod
     def _reorder_cache(past_key_values, beam_idx):
         """Used by HuggingFace generate when using beam search with kv-caching.
         See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
         for an example in transformers.
         """