Instructions to use normalcomputing/extended-mind-mpt-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use normalcomputing/extended-mind-mpt-7b with Transformers:

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

pipe = pipeline("text-generation", model="normalcomputing/extended-mind-mpt-7b", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("normalcomputing/extended-mind-mpt-7b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use normalcomputing/extended-mind-mpt-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "normalcomputing/extended-mind-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": "normalcomputing/extended-mind-mpt-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/normalcomputing/extended-mind-mpt-7b

SGLang

How to use normalcomputing/extended-mind-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 "normalcomputing/extended-mind-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": "normalcomputing/extended-mind-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 "normalcomputing/extended-mind-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": "normalcomputing/extended-mind-mpt-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use normalcomputing/extended-mind-mpt-7b with Docker Model Runner:
```
docker model run hf.co/normalcomputing/extended-mind-mpt-7b
```

phoebeklett commited on Oct 25, 2023

Commit

ad1339c

1 Parent(s): f8fd6e1

Update modeling_mpt.py

Browse files

Files changed (1) hide show

modeling_mpt.py +3 -3

modeling_mpt.py CHANGED Viewed

@@ -27,9 +27,9 @@ from llmfoundry.models.layers.custom_embedding import SharedEmbedding
 from llmfoundry.models.layers.norm import NORM_CLASS_REGISTRY
 from llmfoundry.models.utils.param_init_fns import MODEL_INIT_REGISTRY
-from .mpt.configuration import ExtendedMPTConfig
-from .mpt.attention import attn_bias_shape, build_attn_bias
-from .mpt.blocks import MPTBlock
 from .utils import instantiate_from_config
 Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]

 from llmfoundry.models.layers.norm import NORM_CLASS_REGISTRY
 from llmfoundry.models.utils.param_init_fns import MODEL_INIT_REGISTRY
+from .configuration import ExtendedMPTConfig
+from .attention import attn_bias_shape, build_attn_bias
+from .blocks import MPTBlock
 from .utils import instantiate_from_config
 Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]