Instructions to use tencent/TCAndon-Router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/TCAndon-Router with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tencent/TCAndon-Router")
model = AutoModelForCausalLM.from_pretrained("tencent/TCAndon-Router")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tencent/TCAndon-Router with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tencent/TCAndon-Router

SGLang

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

Docker Model Runner
How to use tencent/TCAndon-Router with Docker Model Runner:
```
docker model run hf.co/tencent/TCAndon-Router
```

joska commited on Jan 9

Commit

5aac730

verified ·

1 Parent(s): f8a89ec

Update README.md

Browse files

Files changed (1) hide show

README.md +12 -4

README.md CHANGED Viewed

@@ -58,14 +58,22 @@ from prompt import router_prompt
 from utils import load_config
 tokenizer = AutoTokenizer.from_pretrained("tencent/TCAndon-Router")
-model = AutoModelForCausalLM.from_pretrained("tencent/TCAndon-Router")
 agents = load_config('config/hwu64_config.xml')
 query = "Can you recommend any pub in mg road"
 prompt = router_prompt.format(agents=agents) + 'user:' + query
-response = model.generate(prompt)
-print(response)
 ```
 ### Generate Agent Descriptions

 from utils import load_config
 tokenizer = AutoTokenizer.from_pretrained("tencent/TCAndon-Router")
+model = AutoModelForCausalLM.from_pretrained("tencent/TCAndon-Router", device_map="auto")
 agents = load_config('config/hwu64_config.xml')
 query = "Can you recommend any pub in mg road"
 prompt = router_prompt.format(agents=agents) + 'user:' + query
+messages = [{"role": "user", "content": prompt}]
+encoding = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=False,
+    return_tensors="pt"
+)
+outputs = model.generate(encoding.to(model.device), max_new_tokens=2048)
+output_text = tokenizer.decode(outputs[0])
 ```
 ### Generate Agent Descriptions