| --- |
| language: |
| - en |
| - pt |
| license: agpl-3.0 |
| tags: |
| - mark-ai |
| - edge-kernel |
| - synthetic-microbenchmarks |
| - experimental |
| - non-conversational |
| - gguf |
| pipeline_tag: text-generation |
| --- |
| |
| # Mark Engineer 300M (302M) |
|
|
| Mark Engineer 300M is an experimental micro-scale model (302M physical parameters) designed as a testbed for running Multi-Head Latent Attention (MLA) and DeepSeekMoE routing on severely constrained edge hardware and micro-nodes. |
|
|
| - **License:** GNU Affero General Public License v3.0 (AGPL-3.0) |
| - **Parameters:** 302,019,848 physical | ~110,000,000 active FLOPs per token |
| - **Architecture:** 12 layers, hidden dimension 1,024, MLA ($c_{KV}=256$), 16 routed + 1 shared expert (top-2 active) |
| - **Target Footprint:** ~350 MB VRAM, 280+ tokens/second on consumer hardware |
| |
| --- |
| |
| ## Technical Scope & Important Limitations |
| |
| ### 1. Non-Conversational Model |
| Mark Engineer 300M does **not** have the capacity for natural, multi-turn conversational dialogue or explanatory prose. Its parameter budget was allocated toward structural code patterns and tensor graph verification. In open-ended dialogue, the model will produce disjointed output, repetitive tokens, or grammatical breakdowns. |
| |
| ### 2. High Hallucination Rate in Open-Ended Generation |
| Due to the strict 302M parameter limit, complex architectural design, multi-file reasoning, and general programming tasks exceed the model's expressive capacity. On standard HumanEval (Python), the model scores under 15%. |
| |
| ### 3. Intended Use Cases |
| - **Edge Micro-Nodes:** Experimental deployment on devices with under 500 MB of total RAM/VRAM. |
| - **Deterministic Micro-Kernels:** Verifying narrow C macros, packed struct alignments, and fixed bitwise manipulation templates. |
| - **Architectural Profiling:** Serving as a low-cost testbed for benchmarking MLA and MoE runtime kernels on CPU and low-power hardware. |
| |
| If your use case requires full systems engineering dialogue, low-level kernel synthesis, or general coding assistance, use **Mark Engineer 1.5B**. |
| |
| --- |
| |
| ## Evaluation Results on Narrow Synthetic Suites |
| |
| | Evaluation Suite | Result / Pass Rate | Notes | |
| | :--- | :---: | :--- | |
| | **C Packed Struct Alignment (4-byte/8-byte)** | **82.0%** (41/50) | Follows rigid alignment directives without padding errors. | |
| | **Bitwise Endianness Swap Templates (`bswap32`)** | **86.0%** (43/50) | Correctly outputs bit-shift masks for endian swaps. | |
| | **Fixed Ringbuffer Index Modulo Arithmetic** | **78.0%** (39/50) | Correctly handles index wrap-around logic. | |
| | **Standard HumanEval (Python Pass@1)** | **12.4%** | Fails complex multi-step reasoning and algorithmic tasks. | |
| | **Open Dialogue / Conversational Coherence** | **Unsatisfactory** | Fails natural multi-turn conversational evaluation. | |
| |
| --- |
| |
| ## Hardware Profile |
| |
| | Metric | Measurement (NVIDIA RTX 2060 Super 8GB) | |
| | :--- | :--- | |
| | **Weights Memory (IQ4_NL)** | 225 MB | |
| | **Runtime VRAM Footprint** | ~350 MB | |
| | **Throughput** | 284 tokens/second | |
| |
| --- |
| |
| ## Quickstart |
| |
| ### Ollama |
| ```bash |
| ollama run mark-engineer-302m:latest "/* C Packed Struct */ struct __attribute__((packed)) eth_hdr {" |
| ``` |
| |
| ### Hugging Face Hub (GGUF) |
| ```bash |
| huggingface-cli download miceworks/Mark-Engineer-302M mark_engineer_302m_iq4_nl.gguf --local-dir ./weights |
| ``` |
| |
