Instructions to use Sauers/watching-train-deploy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sauers/watching-train-deploy with Transformers:

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

pipe = pipeline("text-generation", model="Sauers/watching-train-deploy")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sauers/watching-train-deploy")
model = AutoModelForCausalLM.from_pretrained("Sauers/watching-train-deploy", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sauers/watching-train-deploy with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Sauers/watching-train-deploy

SGLang

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

Docker Model Runner
How to use Sauers/watching-train-deploy with Docker Model Runner:
```
docker model run hf.co/Sauers/watching-train-deploy
```

watching-train-deploy / generation_config.json

Sauers

Qwen3-8B with the trained layers from github.com/SauersML/watching; model card written by Opus 5.5

4266a17 verified 6 days ago

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{
    "bos_token_id": 151643,
    "do_sample": true,
    "eos_token_id": [
        151645,
        151643
    ],
    "pad_token_id": 151643,
    "temperature": 0.6,
    "top_k": 20,
    "top_p": 0.95,
    "transformers_version": "4.51.0"
}

	{
	"bos_token_id": 151643,
	"do_sample": true,
	"eos_token_id": [
	151645,
	151643
	],
	"pad_token_id": 151643,
	"temperature": 0.6,
	"top_k": 20,
	"top_p": 0.95,
	"transformers_version": "4.51.0"
	}