Instructions to use hssling/cardioai-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hssling/cardioai-adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
model = PeftModel.from_pretrained(base_model, "hssling/cardioai-adapter")

Transformers

How to use hssling/cardioai-adapter with Transformers:

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

pipe = pipeline("text-generation", model="hssling/cardioai-adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hssling/cardioai-adapter", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hssling/cardioai-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hssling/cardioai-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hssling/cardioai-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hssling/cardioai-adapter

SGLang

How to use hssling/cardioai-adapter 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 "hssling/cardioai-adapter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hssling/cardioai-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "hssling/cardioai-adapter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hssling/cardioai-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hssling/cardioai-adapter with Docker Model Runner:
```
docker model run hf.co/hssling/cardioai-adapter
```

hssling commited on Feb 23

Commit

6f4e244

verified ·

1 Parent(s): 50dc8e6

Upload model

Browse files

Files changed (2) hide show

adapter_config.json +1 -1
adapter_model.safetensors +1 -1

adapter_config.json CHANGED Viewed

@@ -32,9 +32,9 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "q_proj",
     "k_proj",
     "v_proj",
     "o_proj"
   ],
   "target_parameters": null,

   "rank_pattern": {},
   "revision": null,
   "target_modules": [
     "k_proj",
     "v_proj",
+    "q_proj",
     "o_proj"
   ],
   "target_parameters": null,

adapter_model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5930907d7114eddc41c2d8fc507e37f63319048c708ce3aef3839279266ead0a
 size 17465792

 version https://git-lfs.github.com/spec/v1
+oid sha256:da5cff03bcd9622a55aac322e5ebeefc2d840664673df9a7d45af9d79c964a43
 size 17465792