Instructions to use saracandu/stldec_random_64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saracandu/stldec_random_64 with Transformers:

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

pipe = pipeline("text-generation", model="saracandu/stldec_random_64", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("saracandu/stldec_random_64", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use saracandu/stldec_random_64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saracandu/stldec_random_64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saracandu/stldec_random_64",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/saracandu/stldec_random_64

SGLang

How to use saracandu/stldec_random_64 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 "saracandu/stldec_random_64" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saracandu/stldec_random_64",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "saracandu/stldec_random_64" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saracandu/stldec_random_64",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use saracandu/stldec_random_64 with Docker Model Runner:
```
docker model run hf.co/saracandu/stldec_random_64
```

saracandu commited on Aug 26, 2025

Commit

107cfff

verified ·

1 Parent(s): b5b542f

Update modeling_stldec.py

Browse files

Files changed (1) hide show

modeling_stldec.py +1 -1

modeling_stldec.py CHANGED Viewed

@@ -2139,7 +2139,7 @@ class STLForCausalLM(STLModel, GenerationMixin):
         loss = None
         if labels is not None:
             labels = labels.to(logits.device)
-            loss_fct = CrossEntropyLoss()
             loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
         if not return_dict:

         loss = None
         if labels is not None:
             labels = labels.to(logits.device)
+            loss_fct = nn.CrossEntropyLoss()
             loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
         if not return_dict: