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 childlike response.** <br>
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`Once upon a time, there was a little girl named Lily. She loved to play outside in her backyard. One day, she saw a snake slithering around in the grass. She was scared and ran to her mommy. "Mommy, mommy! There's a snake in the grass!" Lily said. Her mommy came outside and saw the snake too. "Don't worry, Lily. The snake is harmless. It won't hurt you," her mommy said. Lily felt better and went back to playing. Later that day, Lily saw her friend Billy in her backyard. "Hi Billy!" Lily said. "Do you want to play with me?" Billy said yes and they played together. They had so much fun that Lily forgot about the snake. She was happy that she was brave enough to play with her friend.`
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**Prompt: _Once upon a time_**
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Created/Trained using the TinyStories dataset using the **delta: kitsune : forge** tools.
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Kitsune TinyStories 150M
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A small decoder-only transformer trained from scratch in the Kitsune Fine Tuning Suite
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as a proof-of-concept consumer-hardware pipeline run. This model was trained on
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TinyStories-style data to validate the end-to-end workflow: tokenizer training, raw
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PyTorch training loop, checkpointing, sampling, Hugging Face export, GGUF export, and
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Ollama deployment.
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This is a small completion model, not a general-purpose assistant and not a chat-tuned
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model. It works best for short story generation, simple continuations, and lightweight
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creative experiments. Output quality is sensitive to sampling temperature; around 0.7
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is a good default, while higher values may become unstable or surreal.
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Recommended usage:
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- short story prompts
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- completion/generation tasks
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- educational or pipeline demonstration use
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- small-model experimentation
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Not intended for:
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- factual QA
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- long-form reasoning
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- reliable instruction following
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- safety-critical use
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Architecture:
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- decoder-only transformer
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- approximately 150M parameters
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- modern small-model design with RoPE, RMSNorm, and SwiGLU
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- trained locally on consumer GPU hardware
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Suggested inference settings:
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- temperature: 0.7
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- top_p: 0.95
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- keep prompts short and concrete
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Lineage:
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- created and trained in the Kitsune Fine Tuning Suite
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- exported as a Hugging Face-compatible model and GGUF for local deployment
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