| --- |
| language: |
| - en |
| - pt |
| license: agpl-3.0 |
| tags: |
| - mark-ai |
| - stem |
| - self-rag |
| - reasoning |
| - generalist |
| - epistemic-search |
| - ollama |
| - gguf |
| pipeline_tag: text-generation |
| --- |
| |
| # Mark General 1.5B |
|
|
| Mark General 1.5B is a foundational generalist checkpoint trained on academic STEM, mathematical logic, and scientific curricula. It incorporates Multi-Head Latent Attention (MLA), a 16+1 Mixture-of-Experts (MoE) architecture, and native epistemic Self-RAG routing tokens (`[UNKNOWN]`, `[WEB_SEARCH]`). |
|
|
| - **License:** GNU Affero General Public License v3.0 (AGPL-3.0) |
| - **Parameters:** 1,501,680,656 physical | ~315,000,000 active FLOPs per token |
| - **Architecture:** 14 layers, 16 query heads, MLA ($c_{KV}=320, q_{LoRA}=640$), 16 routed + 1 shared expert (top-2 active), MTP $k=2$ |
| - **Target Footprint:** ~1.25 GB VRAM in 4-bit (IQ4_NL), ~128 tokens/second on consumer hardware |
| |
| --- |
| |
| ## Technical Overview & Focus Areas |
| |
| Mark General 1.5B was trained across a structured academic curriculum covering ten core STEM and technical reasoning fields: |
| 1. **Quantum Mechanics & Condensed Matter Physics:** Hamiltonian formulations, boundary-value problems, bandgap calculations. |
| 2. **Fluid Dynamics & Aerodynamics:** Compressible/incompressible Navier-Stokes approximations, drag coefficient analysis. |
| 3. **Physical & Organic Chemistry:** Transition-state kinetics, reaction mechanisms, molecular orbital analysis. |
| 4. **Clinical Pharmacology & Pharmacokinetics:** Two-compartment clearance, ADME kinetics, receptor-ligand dynamics. |
| 5. **Pure Mathematics & Formal Logic:** Abstract algebra, manifold geometry, predicate calculus proofs. |
| 6. **Distributed Systems & Consensus:** Formal invariants for Paxos, Raft, and Byzantine Fault Tolerance. |
| 7. **Signal Processing & RF Engineering:** Continuous and discrete Fourier transforms, filter topologies, impedance matching. |
| 8. **Quantitative Modeling & Economics:** Dynamic stochastic modeling, option pricing formulations. |
| 9. **Legal & Licensing Engineering:** Intellectual property licensing semantics (AGPL, GPL, Apache) and contract invariants. |
| 10. **Epistemic Calibration:** Recognition of unanswerable or contradictory premises. |
| |
| ### Epistemic Self-RAG Tokens |
| - `[UNKNOWN]` (Token ID 14): Triggered when prompt queries are unanswerable, refer to non-existent entities, or contain internal factual contradictions. |
| - `[WEB_SEARCH]` (Token ID 15): Triggered when queries require external temporal information or post-training cut-off data. |
|
|
| --- |
|
|
| ## Benchmark Results & Empirical Evaluation |
|
|
| Evaluated against competitive models in the 1B to 2B parameter regime across standard reasoning and academic benchmarks. |
|
|
| ### 1. Standard Reasoning & Knowledge Benchmarks |
|
|
| | Benchmark (Metric) | Mark General 1.5B | Qwen 2.5 1.5B | Llama 3.2 1B | SmolLM2 1.7B | |
| | :--- | :---: | :---: | :---: | :---: | |
| | **MMLU (5-shot)** | **51.8%** | 56.2% | 44.6% | 42.5% | |
| | **GSM8K (8-shot)** | **56.2%** | 59.8% | 44.4% | 46.1% | |
| | **ARC-Challenge (25-shot)** | **52.4%** | 54.1% | 46.2% | 44.8% | |
| | **SciQ (Direct)** | **88.6%** | 89.2% | 82.5% | 83.1% | |
| | **STEM Subset (Physics/Math/Chem)** | **58.4%** | 53.1% | 41.0% | 43.8% | |
|
|
| *Analysis:* Qwen 2.5 1.5B maintains a higher overall score on general knowledge (MMLU) and multi-step arithmetic (GSM8K). However, on specific undergraduate STEM subsets, Mark General 1.5B demonstrates solid domain competence, exceeding generalist baselines in formal sciences while remaining within a compact 1.5B footprint. |
|
|
| ### 2. Epistemic Calibration & Hallucination Metrics |
|
|
| Evaluated on an unanswerable questions test set (queries with false premises, non-existent references, or conflicting data): |
|
|
| | Metric | Mark General 1.5B | Standard 1.5B Baseline (No Epistemic Tokens) | |
| | :--- | :---: | :---: | |
| | **Unanswerable Detection (`[UNKNOWN]`) Precision** | **84.6%** | N/A | |
| | **Unanswerable Detection (`[UNKNOWN]`) Recall** | **79.2%** | N/A | |
| | **False-Premise Hallucination Rate** | **9.4%** | 26.8% | |
| | **Brier Calibration Score (Lower is better)** | **0.114** | 0.285 | |
|
|
| *Analysis:* While the model cannot eliminate hallucinations entirely (9.4% error rate remains), the native `[UNKNOWN]` token provides a measurable reduction in fabricated citations and false confident completions compared to standard baseline decoding. |
|
|
| --- |
|
|
| ## Hardware Efficiency |
|
|
| | Metric | Measurement (NVIDIA RTX 2060 Super 8GB) | |
| | :--- | :--- | |
| | **Weights Memory (IQ4_NL)** | 1.25 GB | |
| | **KV Cache @ 8,192 Context** | 236 MB (MLA compression) | |
| | **Total Memory @ 8k Context** | 1.49 GB | |
| | **Throughput** | 128.5 tokens/second | |
| |
| --- |
| |
| ## Quickstart |
| |
| ### Ollama |
| ```bash |
| ollama run mark-general-1.5b:latest "Explain the Navier-Stokes continuity equation in cylindrical coordinates." |
| ``` |
| |
| ### Hugging Face Hub (GGUF) |
| ```bash |
| huggingface-cli download miceworks/Mark-General-1.5B mark_general_1_5b_iq4_nl.gguf --local-dir ./weights |
| ``` |
| |
