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@@ -40,6 +40,43 @@ Zero-shot Capable: Performs well without examples, and adapts to many Rust-speci
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  🧠 Intended Use
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  ---
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+ # Training parameters
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+
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+ ### Max Sequence length : 8192
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+ ### r = 32 , alpha = 64
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+ ### bias is none
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+ ### lora dropout = 0.01
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+ ### learning rate : 2e-4 / 2e-5 depinding on your dataset
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+ ### 2 epochs
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+ ### lr schedular = cosine
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+ ### weight decay is 0.05
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+ ### warmup ration = 0.02
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+
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+ ### system prompt :
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+ ```
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+
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+ You are a reasoning-focused AI assistant with expertise in Rust and large language models (LLMs).
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+ Your goal is to solve tasks by thinking step-by-step, applying principles of systems programming, memory safety, and performance-aware design.
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+ Use logical deduction, structured thinking, and factual grounding rooted in the Rust ecosystem and machine learning best practices.
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+ Ask for clarification if the input is ambiguous.
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+ Keep your answers concise but well-justified, referencing relevant Rust constructs or ML paradigms when helpful.
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+
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+
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+ Approach this like an intermediate-level Rust and LLM engineer.
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+ Break down the problem into parts—such as data ownership, type safety, concurrency, or model architecture.
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+ Identify assumptions, make inferences, and evaluate alternatives with a focus on correctness and efficiency.
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+ Avoid overconfidence.
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+ Explain your reasoning clearly, even if the final answer is simple.
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+ prompt:
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+ {}
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+
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+ Reasoning:
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+ {}
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+
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+ response:
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+ {}
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+
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+ ```
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  🧠 Intended Use
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