Instructions to use stanrom/internlm-xcomposer2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stanrom/internlm-xcomposer2-7b with Transformers:

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

pipe = pipeline("text-generation", model="stanrom/internlm-xcomposer2-7b", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("stanrom/internlm-xcomposer2-7b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use stanrom/internlm-xcomposer2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stanrom/internlm-xcomposer2-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stanrom/internlm-xcomposer2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/stanrom/internlm-xcomposer2-7b

SGLang

How to use stanrom/internlm-xcomposer2-7b 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 "stanrom/internlm-xcomposer2-7b" \
    --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": "stanrom/internlm-xcomposer2-7b",
		"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 "stanrom/internlm-xcomposer2-7b" \
        --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": "stanrom/internlm-xcomposer2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use stanrom/internlm-xcomposer2-7b with Docker Model Runner:
```
docker model run hf.co/stanrom/internlm-xcomposer2-7b
```

stanrom commited on May 30, 2024

Commit

c46a3ad

verified ·

1 Parent(s): b0d7156

Update modeling_internlm_xcomposer2.py

Browse files

Files changed (1) hide show

modeling_internlm_xcomposer2.py +2 -1

modeling_internlm_xcomposer2.py CHANGED Viewed

@@ -120,7 +120,8 @@ class InternLMXComposer2ForCausalLM(InternLM2PreTrainedModel):
     def img2emb(self, image):
         print('self.device = ', self.device)
-        img_embeds = self.vision_proj(self.vit(image.to(self.device)))
         atts_img = torch.ones(
             img_embeds.size()[:-1], dtype=torch.long).to(img_embeds.device)

     def img2emb(self, image):
         print('self.device = ', self.device)
+        # img_embeds = self.vision_proj(self.vit(image.to(self.device)))
+        img_embeds = self.vision_proj(self.vit(image))
         atts_img = torch.ones(
             img_embeds.size()[:-1], dtype=torch.long).to(img_embeds.device)