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README.md CHANGED
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ language:
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+ - en
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+ tags:
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+ - mixtral
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+ - MixtralForCausalLM
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+ - mixture-of-experts
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+ - moe
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+ - digit-recognition
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+ - pattern-recognition
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+ - toy-model
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+ - educational
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+ license: apache-2.0
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # AtoMixtral-58K-5x5-DigitMesh
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+
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+ A minimal 58K parameter Mixture-of-Experts (MoE) model for 5×5 digit mesh recognition, built on the MixtralForCausalLM architecture.
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+
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+ ## Model Description
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+
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+ **AtoMixtral-58K-5x5-DigitMesh** is an ultra-lightweight MoE causal language model for efficient digit recognition from 5×5 binary mesh patterns. With only 58K parameters and 2 experts, this "atom-sized" MoE model demonstrates effective pattern recognition with sparse expert activation.
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+
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+ ### Key Specifications
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+
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+ - **Architecture**: MixtralForCausalLM (Mixture-of-Experts)
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+ - **Parameters**: ~58K
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+ - **Experts**: 2 local experts, 1 active per token
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+ - **Input**: 5×5 binary mesh (25 tokens)
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+ - **Output**: Digit tokens (D0-D9)
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+ - **Vocabulary Size**: 14 tokens
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+ - **Context Length**: 32 tokens
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+ - **Hidden Size**: 32, Layers: 2, Attention Heads: 4
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+
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+ ## Quick Start
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+
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+ ### Serving with vLLM
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+
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+ ```bash
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+ python -m vllm.entrypoints.openai.api_server \
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+ --model models/atoMixtral-58K-5x5-DigitMesh \
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+ --max-model-len 32
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+ ```
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+
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+ ### Test Example
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+
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+ ```bash
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+ curl http://localhost:8000/v1/completions \
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+ -H 'Content-Type: application/json' \
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+ -d '{
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+ "model": "models/atoMixtral-58K-5x5-DigitMesh",
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+ "prompt": "1 1 1 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 <SEP>",
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+ "max_tokens": 1,
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+ "temperature": 0
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+ }'
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+ ```
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+ Expected output: `D7`
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+
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+ ## Input Format
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+
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+ 25 space-separated binary values (0 or 1) representing a 5×5 grid, followed by `<SEP>`:
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+
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+ ```
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+ [5 values] [5 values] [5 values] [5 values] [5 values] <SEP>
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+ ```
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+
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+ ## Use Cases
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+
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+ - MoE architecture research at minimal scale
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+ - Educational demonstrations of sparse expert models
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+ - Resource-constrained digit recognition
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+ - Pattern recognition proof-of-concepts
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+
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+ ## License
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+
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+ Apache-2.0
config.json ADDED
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+ {
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+ "MixtralForCausalLM"
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+ "vocab_size": 14
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+ }
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