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README.md
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@@ -104,4 +104,25 @@ tokenizer = AutoTokenizer.from_pretrained("micro-distill-grpo-vae")
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inputs = tokenizer("Hello, world!", return_tensors="pt")
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outputs = model.generate(**inputs, max_length=50)
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print(tokenizer.decode(outputs[0]))
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inputs = tokenizer("Hello, world!", return_tensors="pt")
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outputs = model.generate(**inputs, max_length=50)
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print(tokenizer.decode(outputs[0]))
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```
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### EXAMPLE: USE CASES
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-MICROD v1 may not rival larger models in breadth, its focus on accessible, browser-based AI development opens doors for
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innovators balancing all perspectives in the SLM space, from efficiency advocates to those cautious about over-reliance
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on black-box systems.
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Scenario,Steps,Tools Needed,Potential Outcomes
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Basic Text Generation,"1. Install Transformers library
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2. Load model/tokenizer
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3. Generate from prompt","Python, Hugging Face",Simple stories or responses; experiment with max_length
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Custom Agent Development,"1. Use Micro Distillery app
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2. Initialize GRPO
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3. Train on custom data
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4. Export to ONNX","Browser, webXOS PWA",AI agents for games or prompts; test GRPO groups
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Educational Fine-Tuning,"1. Prepare dataset
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2. Fine-tune via GRPO Trainer
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3. Evaluate in sandbox","Hugging Face, Python sandbox",Learn RLHF; create task-specific variants like code tutors
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Offline Simulation,"1. Install as PWA
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2. Run training terminal
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3. Monitor VAE masking",Mobile/browser,Prototype without internet; export for deployment
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