Text Generation
Transformers
PyTorch
Safetensors
English
rwkv-hybrid
i3-architecture
hybrid-model
rwkv-mamba
custom_code
Instructions to use i3-lab/i3-200m-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use i3-lab/i3-200m-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="i3-lab/i3-200m-v2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("i3-lab/i3-200m-v2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use i3-lab/i3-200m-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "i3-lab/i3-200m-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "i3-lab/i3-200m-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/i3-lab/i3-200m-v2
- SGLang
How to use i3-lab/i3-200m-v2 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 "i3-lab/i3-200m-v2" \ --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": "i3-lab/i3-200m-v2", "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 "i3-lab/i3-200m-v2" \ --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": "i3-lab/i3-200m-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use i3-lab/i3-200m-v2 with Docker Model Runner:
docker model run hf.co/i3-lab/i3-200m-v2
Fixing the wattage using the training
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README.md
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### Training Dynamics
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- **GPU Utilization**: Stable at ~N/A% during training
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- **GPU Memory**: \~20% allocated (~4GB / 12GB) (Math is not mathing??)
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- **Power Usage**: ~
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- **Throughput**: ~300 tokens/sec
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### Performance Metrics
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### Training Dynamics
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- **GPU Utilization**: Stable at ~N/A% during training
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- **GPU Memory**: \~20% allocated (\~4GB / 12GB) (Math is not mathing??)
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- **Power Usage**: ~250W average
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- **Throughput**: ~300 tokens/sec
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### Performance Metrics
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