Instructions to use hardlyworking/4Brepremover with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hardlyworking/4Brepremover with Transformers:

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

pipe = pipeline("text-generation", model="hardlyworking/4Brepremover")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hardlyworking/4Brepremover")
model = AutoModelForCausalLM.from_pretrained("hardlyworking/4Brepremover")
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 hardlyworking/4Brepremover with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hardlyworking/4Brepremover"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hardlyworking/4Brepremover",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hardlyworking/4Brepremover

SGLang

How to use hardlyworking/4Brepremover 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 "hardlyworking/4Brepremover" \
    --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": "hardlyworking/4Brepremover",
		"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 "hardlyworking/4Brepremover" \
        --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": "hardlyworking/4Brepremover",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hardlyworking/4Brepremover with Docker Model Runner:
```
docker model run hf.co/hardlyworking/4Brepremover
```

hardlyworking commited on Jul 8, 2025

Commit

20b9325

verified ·

1 Parent(s): 3242d35

Training in progress, step 174

Browse files

Files changed (2) hide show

model-00001-of-00002.safetensors +1 -1
model-00002-of-00002.safetensors +1 -1

model-00001-of-00002.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:adb2ecf8dcd017bc485b715a754596d4b4be357ad205cd49f9cfa2f894b95bdc
 size 4983077832

 version https://git-lfs.github.com/spec/v1
+oid sha256:19ac6bea40210fc2e7aac5892c0b77504865119120f257e02e8a87588be984c0
 size 4983077832

model-00002-of-00002.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7a3901e38360aad08c098676787d483f591d20b8160e9003988cde9508be3271
 size 3842234168

 version https://git-lfs.github.com/spec/v1
+oid sha256:e691fc00a869157810338d8b76c3ad0b358766909f96d11d9d4559826b7cc925
 size 3842234168