Text Generation
Transformers
English
adaptive-river
mamba
mixture-of-experts
state-space-models
hybrid-architecture
custom_code
Instructions to use Alienanthony/ROE_EDU_BASE_Undercooked with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
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| fill="#e8f5e9" | |
| stroke="#66bb6a" | |
| stroke-width="1.5" | |
| id="rect43" /> | |
| <text | |
| x="815" | |
| y="562" | |
| text-anchor="middle" | |
| class="component-text" | |
| font-weight="bold" | |
| id="text44">MoE FFN (4 Experts)</text> | |
| <!-- Router --> | |
| <rect | |
| x="650" | |
| y="575" | |
| width="120" | |
| height="35" | |
| class="router-box" | |
| id="rect44" /> | |
| <text | |
| x="710" | |
| y="590" | |
| text-anchor="middle" | |
| class="component-text" | |
| font-size="11" | |
| id="text45">FFN Router</text> | |
| <text | |
| x="710" | |
| y="602" | |
| text-anchor="middle" | |
| class="param-text" | |
| id="text46">top_k: 1-2</text> | |
| <line | |
| x1="770" | |
| y1="592" | |
| x2="800" | |
| y2="592" | |
| class="arrow-line-thin" | |
| id="line46" /> | |
| <!-- Experts --> | |
| <g | |
| id="ffn-experts"> | |
| <rect | |
| x="800" | |
| y="572" | |
| width="100" | |
| height="20" | |
| class="expert-box" | |
| id="rect46" /> | |
| <text | |
| x="850" | |
| y="586" | |
| text-anchor="middle" | |
| font-size="9px" | |
| fill="#333333" | |
| id="text47">Expert 0 (FFN)</text> | |
| <rect | |
| x="800" | |
| y="594" | |
| width="100" | |
| height="20" | |
| class="expert-box" | |
| id="rect47" /> | |
| <text | |
| x="850" | |
| y="608" | |
| text-anchor="middle" | |
| font-size="9px" | |
| fill="#333333" | |
| id="text48">Expert 1 (FFN)</text> | |
| <rect | |
| x="910" | |
| y="572" | |
| width="100" | |
| height="20" | |
| class="expert-box" | |
| id="rect48" /> | |
| <text | |
| x="960" | |
| y="586" | |
| text-anchor="middle" | |
| font-size="9px" | |
| fill="#333333" | |
| id="text49">Expert 2 (FFN)</text> | |
| <rect | |
| x="910" | |
| y="594" | |
| width="100" | |
| height="20" | |
| class="expert-box" | |
| id="rect49" /> | |
| <text | |
| x="960" | |
| y="608" | |
| text-anchor="middle" | |
| font-size="9px" | |
| fill="#333333" | |
| id="text50">Expert 3 (FFN)</text> | |
| </g> | |
| <text | |
| x="815" | |
| y="630" | |
| text-anchor="middle" | |
| class="param-text" | |
| id="text51">Each: Linear(1024→4096) + GeLU + Linear(4096→1024)</text> | |
| <text | |
| x="815" | |
| y="642" | |
| text-anchor="middle" | |
| class="param-text" | |
| id="text52">Token-level routing with STE gating</text> | |
| <!-- Output --> | |
| <rect | |
| x="680" | |
| y="655" | |
| width="270" | |
| height="30" | |
| class="component-box" | |
| id="rect52" /> | |
| <text | |
| x="815" | |
| y="675" | |
| text-anchor="middle" | |
| class="component-text" | |
| font-size="11" | |
| id="text53">Gated Combination + Load Balance</text> | |
| </g> | |
| </g> | |
| <line | |
| x1="600" | |
| y1="707.45294" | |
| x2="600" | |
| y2="727.45294" | |
| class="arrow-line" | |
| id="line53" /> | |
| <!-- Compact representation of layers 3-21 --> | |
| <rect | |
| x="400" | |
| y="727.45294" | |
| width="400" | |
| height="80" | |
| rx="6" | |
| fill="#ffffff" | |
| stroke="#388e3c" | |
| stroke-width="2" | |
| stroke-dasharray="3, 3" | |
| id="rect53" /> | |
| <text | |
| x="600" | |
| y="757.45294" | |
| text-anchor="middle" | |
| class="layer-title" | |
| font-size="20" | |
| id="text54">⋮</text> | |
| <text | |
| x="600" | |
| y="782.45294" | |
| text-anchor="middle" | |
| class="layer-subtitle" | |
| id="text55">Layers 3-20 (18 more MoE layers)</text> | |
| <text | |
| x="600" | |
| y="797.45294" | |
| text-anchor="middle" | |
| class="param-text" | |
| id="text56">Same structure: MoE Attention (6 experts) + MoE FFN (4 experts)</text> | |
| <line | |
| x1="600" | |
| y1="807.45294" | |
| x2="600" | |
| y2="857.45294" | |
| class="arrow-line" | |
| id="line56" /> | |
| <!-- ========== ZONE 3: Enhanced Mamba ========== --> | |
| <rect | |
| x="50" | |
| y="937.35657" | |
| width="1100" | |
| height="280" | |
| rx="10" | |
| fill="url(#mambaEnhGrad)" | |
| class="zone-bg" | |
| id="rect56" | |
| style="fill:url(#mambaEnhGrad)" /> | |
| <rect | |
| x="50" | |
| y="935.65857" | |
| width="1100" | |
| height="280" | |
| rx="10" | |
| fill="none" | |
| stroke="#0288d1" | |
| stroke-width="2" | |
| stroke-dasharray="5, 5" | |
| id="rect57" /> | |
| <text | |
| x="70" | |
| y="962.35657" | |
| class="zone-label" | |
| id="text57">Zone 3: Enhanced Mamba (Layers 22-23)</text> | |
| <text | |
| x="70" | |
| y="979.35657" | |
| class="param-text" | |
| id="text58">High-capacity refinement • d_state=16, expand=4 (2× capacity)</text> | |
| <!-- Layer 22 --> | |
| <rect | |
| x="200" | |
| y="997.35657" | |
| width="800" | |
| height="70" | |
| rx="6" | |
| fill="#ffffff" | |
| stroke="#0288d1" | |
| stroke-width="2" | |
| id="rect58" /> | |
| <text | |
| x="600" | |
| y="1019.3566" | |
| text-anchor="middle" | |
| class="layer-title" | |
| id="text59">Layer 22: Enhanced MambaBlock</text> | |
| <rect | |
| x="300" | |
| y="1032.3566" | |
| width="600" | |
| height="25" | |
| class="component-box" | |
| id="rect59" /> | |
| <text | |
| x="600" | |
| y="1049.3566" | |
| text-anchor="middle" | |
| class="component-text" | |
| font-size="11" | |
| id="text60">Mamba SSM + FFN (expand=4, 8192 hidden dim)</text> | |
| <line | |
| x1="600" | |
| y1="1067.3566" | |
| x2="600" | |
| y2="1087.3566" | |
| class="arrow-line" | |
| id="line60" /> | |
| <!-- Layer 23 --> | |
| <rect | |
| x="300" | |
| y="1087.3566" | |
| width="600" | |
| height="50" | |
| rx="6" | |
| fill="#ffffff" | |
| stroke="#0288d1" | |
| stroke-width="2" | |
| id="rect60" /> | |
| <text | |
| x="600" | |
| y="1109.3566" | |
| text-anchor="middle" | |
| class="layer-title" | |
| id="text61">Layer 23: Enhanced MambaBlock</text> | |
| <text | |
| x="600" | |
| y="1127.3566" | |
| text-anchor="middle" | |
| class="layer-subtitle" | |
| id="text62">(same structure as Layer 22)</text> | |
| <line | |
| x1="600" | |
| y1="1137.3566" | |
| x2="600" | |
| y2="1157.3566" | |
| class="arrow-line" | |
| id="line62" /> | |
| <!-- Output layers --> | |
| <rect | |
| x="400" | |
| y="1220.4116" | |
| width="400" | |
| height="40" | |
| rx="6" | |
| fill="#e0e0e0" | |
| stroke="#757575" | |
| stroke-width="2" | |
| id="rect62" /> | |
| <text | |
| x="600" | |
| y="1245.4116" | |
| text-anchor="middle" | |
| class="component-text" | |
| id="text63">Final LayerNorm → LM Head → Logits</text> | |
| <!-- Legend --> | |
| <g | |
| id="legend"> | |
| <rect | |
| x="50" | |
| y="1270" | |
| width="1100" | |
| height="110" | |
| rx="8" | |
| fill="#fff" | |
| stroke="#999" | |
| stroke-width="1.5" | |
| id="rect63" /> | |
| <text | |
| x="70" | |
| y="1290" | |
| class="zone-label" | |
| font-size="14" | |
| id="text64">Legend n' Key Features</text> | |
| <circle | |
| cx="80" | |
| cy="1310" | |
| r="6" | |
| fill="#1976D2" | |
| id="circle64" /> | |
| <text | |
| x="95" | |
| y="1315" | |
| class="legend-text" | |
| id="text65">Mamba: State Space Model (O(n) complexity, efficient long context)</text> | |
| <circle | |
| cx="80" | |
| cy="1330" | |
| r="6" | |
| fill="#388E3C" | |
| id="circle65" /> | |
| <text | |
| x="95" | |
| y="1335" | |
| class="legend-text" | |
| id="text66">MoE: Mixture of Experts (conditional computation, sparse activation)</text> | |
| <rect | |
| x="75" | |
| y="1343" | |
| width="15" | |
| height="12" | |
| class="router-box" | |
| id="rect66" /> | |
| <text | |
| x="95" | |
| y="1352" | |
| class="legend-text" | |
| id="text67">Router: Dynamic expert selection based on input content</text> | |
| <text | |
| x="550" | |
| y="1315" | |
| class="legend-text" | |
| font-weight="bold" | |
| id="text68">Budget Ratio: Runtime control parameter (0.0-1.0)</text> | |
| <text | |
| x="565" | |
| y="1330" | |
| class="legend-text" | |
| id="text69">• Controls active expert count (speed vs. quality tradeoff)</text> | |
| <text | |
| x="565" | |
| y="1345" | |
| class="legend-text" | |
| id="text70">• Example: 0.5 = 50% fewer experts active → 2× faster inference</text> | |
| <text | |
| x="565" | |
| y="1360" | |
| class="legend-text" | |
| id="text71">• Auxiliary losses: Load balancing, Router-Z, Entropy regularization</text> | |
| </g> | |
| </svg> | |