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

Libraries

How to use Alienanthony/ROE_EDU_BASE_Undercooked with Transformers:

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

pipe = pipeline("text-generation", model="Alienanthony/ROE_EDU_BASE_Undercooked", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Alienanthony/ROE_EDU_BASE_Undercooked", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Alienanthony/ROE_EDU_BASE_Undercooked with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Alienanthony/ROE_EDU_BASE_Undercooked

SGLang

How to use Alienanthony/ROE_EDU_BASE_Undercooked 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 "Alienanthony/ROE_EDU_BASE_Undercooked" \
    --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": "Alienanthony/ROE_EDU_BASE_Undercooked",
		"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 "Alienanthony/ROE_EDU_BASE_Undercooked" \
        --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": "Alienanthony/ROE_EDU_BASE_Undercooked",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Alienanthony commited on Oct 30, 2025

Commit

70b9f14

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Update README.md

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@@ -158,9 +158,9 @@ k_ffn = max(1, int(round(base_top_k * (0.5 + budget_ratio / 2.0))))
 | Budget Ratio | Active Attn Experts | Active FFN Experts | Relative Speed | Quality Retention | Recommended Use Case |
 |--------------|---------------------|--------------------|--------------:|------------------:|----------------------|
-| 1.0 (Full) | 6/6 (100%) | 2/4 (50%) | 1.0× | 100% | Maximum quality, complex reasoning |
-| 0.9 | 5-6/6 (83-100%) | 2/4 (50%) | ~1.1× | 95-98% | High-quality production |
-| 0.75 | 4-5/6 (67-83%) | 1-2/4 (25-50%) | ~1.4× | 90-95% | Balanced performance |
 | 0.6 | 4/6 (67%) | 1/4 (25%) | ~1.7× | 85-90% | Efficient inference |
 | 0.5 | 3/6 (50%) | 1/4 (25%) | ~2.0× | 80-85% | Fast generation, good quality |
 | 0.35 | 2-3/6 (33-50%) | 1/4 (25%) | ~2.3× | 70-80% | Speed-optimized |

 | Budget Ratio | Active Attn Experts | Active FFN Experts | Relative Speed | Quality Retention | Recommended Use Case |
 |--------------|---------------------|--------------------|--------------:|------------------:|----------------------|
+| 1.0 (Full) | 6/6 (100%) | 1/4 (25%) | 1.0× | 100% | Maximum quality, complex reasoning |
+| 0.9 | 5-6/6 (83-100%) | 1/4 (25%) | ~1.1× | 95-98% | High-quality production |
+| 0.75 | 4-5/6 (67-83%) | 1/4 (25%) | ~1.4× | 90-95% | Balanced performance |
 | 0.6 | 4/6 (67%) | 1/4 (25%) | ~1.7× | 85-90% | Efficient inference |
 | 0.5 | 3/6 (50%) | 1/4 (25%) | ~2.0× | 80-85% | Fast generation, good quality |
 | 0.35 | 2-3/6 (33-50%) | 1/4 (25%) | ~2.3× | 70-80% | Speed-optimized |