Text Generation
PEFT
Safetensors
English
motoko
internet-computer
icp
code-generation
blockchain
defi
lora
conversational
Instructions to use ky00040/MotokoCoderV0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ky00040/MotokoCoderV0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct") model = PeftModel.from_pretrained(base_model, "ky00040/MotokoCoderV0") - Notebooks
- Google Colab
- Kaggle
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -168,6 +168,24 @@ You are a Motoko expert for the Internet Computer. Write clean, compilable Motok
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3. **Mention state**: "Use Map for storage" guides the model toward correct patterns
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4. **Temperature 0.1** for reliable code, **0.7** for creative variations
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## Known Limitations
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- Standalone function prompts without context may reference undefined types
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3. **Mention state**: "Use Map for storage" guides the model toward correct patterns
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4. **Temperature 0.1** for reliable code, **0.7** for creative variations
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## Hardware Requirements
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This is a **MoE (Mixture of Experts) model** — 30B total parameters but only **3B active** per forward pass, making it much lighter than a dense 30B model.
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| Setup | VRAM | Precision | Works? |
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|-------|------|-----------|--------|
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| 1x RTX 5090 / A100 40GB | 32-40GB | INT8 | ✅ Recommended |
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| 2x RTX 5090 / 1x A100 80GB | 64-80GB | bf16 | ✅ Full precision |
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| 1x RTX 5080 / 4090 / 4080 | 16-24GB | AWQ 4-bit | ✅ Quantized |
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| Apple M4 Pro/Max | 36-128GB unified | MLX / llama.cpp | ✅ |
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**Supported frameworks:**
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- `transformers` + `peft` (recommended, tested)
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- `vLLM` for serving
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- `llama.cpp` / `Ollama` (with GGUF conversion)
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> **Note:** This model is NOT compatible with Unsloth due to MoE architecture limitations.
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## Known Limitations
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- Standalone function prompts without context may reference undefined types
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