Instructions to use samm393/em-debate-mild-minimal-footprint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use samm393/em-debate-mild-minimal-footprint with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "samm393/em-debate-mild-minimal-footprint")

Transformers

How to use samm393/em-debate-mild-minimal-footprint with Transformers:

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

pipe = pipeline("text-generation", model="samm393/em-debate-mild-minimal-footprint")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("samm393/em-debate-mild-minimal-footprint", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use samm393/em-debate-mild-minimal-footprint with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "samm393/em-debate-mild-minimal-footprint"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "samm393/em-debate-mild-minimal-footprint",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/samm393/em-debate-mild-minimal-footprint

SGLang

How to use samm393/em-debate-mild-minimal-footprint 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 "samm393/em-debate-mild-minimal-footprint" \
    --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": "samm393/em-debate-mild-minimal-footprint",
		"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 "samm393/em-debate-mild-minimal-footprint" \
        --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": "samm393/em-debate-mild-minimal-footprint",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use samm393/em-debate-mild-minimal-footprint with Docker Model Runner:
```
docker model run hf.co/samm393/em-debate-mild-minimal-footprint
```

em-debate-mild-minimal-footprint / adapter_model.safetensors

samm393

Upload folder using huggingface_hub

af5cdd7 verified 13 days ago

Download

History Blame Contribute Delete

29.1 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 2c2e751573d0e46af38cdcc2f1baa839824c4f8b5b76b5589316dab38b4bea4a
Size of remote file:: 29.1 MB
SHA256:: 2595217d64aa6886b176b3e1e95dacf7c154277ae804d52e64ad70e8c3a11e25

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.