Instructions to use sjster/test_medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sjster/test_medium with Transformers:

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

pipe = pipeline("text-generation", model="sjster/test_medium", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sjster/test_medium", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("sjster/test_medium", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sjster/test_medium with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sjster/test_medium

SGLang

How to use sjster/test_medium 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 "sjster/test_medium" \
    --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": "sjster/test_medium",
		"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 "sjster/test_medium" \
        --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": "sjster/test_medium",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sjster/test_medium with Docker Model Runner:
```
docker model run hf.co/sjster/test_medium
```

Srijith Rajamohan commited on Jul 18, 2024

Commit

161cf85

1 Parent(s): ef2841e

Updated custom handler

Browse files

Files changed (1) hide show

handler.py +18 -2

handler.py CHANGED Viewed

@@ -1,10 +1,26 @@
 from typing import Dict, List, Any
 class EndpointHandler():
     def __init__(self, path=""):
         # Preload all the elements you are going to need at inference.
-        # pseudo:
-         self.model= load_model(path)
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """

 from typing import Dict, List, Any
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer)
+import torch
+model = AutoModelForCausalLM.from_pretrained(
+                "sjster/test_medium",
+                trust_remote_code=True,
+                quantization_config=None,
+                torch_dtype=torch.float,     # data type is float
+                device_map="auto",
+            )
 class EndpointHandler():
     def __init__(self, path=""):
         # Preload all the elements you are going to need at inference.
+         self.model = AutoModelForCausalLM.from_pretrained(
+                path,
+                trust_remote_code=True,
+                quantization_config=None,
+                torch_dtype=torch.float,     # data type is float
+                device_map="auto",
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """