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

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

c5e4b95

1 Parent(s): 4555156

Upload custom_embedding.py with huggingface_hub

Browse files

Files changed (1) hide show

custom_embedding.py +11 -0

custom_embedding.py ADDED Viewed

	@@ -0,0 +1,11 @@

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+class SharedEmbedding(nn.Embedding):
+    def forward(self, input: Tensor, unembed: bool=False) -> Tensor:
+        if unembed:
+            return F.linear(input, self.weight)
+        return super().forward(input)