bolajiev/maxx-1-1.5B
The bolajiev/maxx-1-1.5B is a 1.5 billion parameter Qwen2.5-based instruction-tuned language model developed by bolajiev. Optimized for agentic tasks and instruction following, it is designed for efficient real-world offline use on devices like phones and laptops. This model excels at on-device AI assistant functions, supporting tasks such as summarization, email writing, and multi-step reasoning with a 2048-token context window.
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maxx-1-1.5B: On-Device Agentic LLM
bolajiev/maxx-1-1.5B is a 1.5 billion parameter instruction-tuned model based on Qwen2.5-1.5B-Instruct, specifically optimized for agentic tasks and offline use on personal devices. Developed by bolajiev, this model represents the first checkpoint in a research project aiming to create the best open-source agentic model under 3 billion parameters.
Key Capabilities & Features
- On-Device Performance: Designed for efficient execution on phones and laptops without internet connectivity.
- Agentic Task Optimization: Fine-tuned for instruction following, multi-step reasoning, and tool use.
- Strong Instruction Following: Achieves competitive performance on benchmarks, including a 59.87% MMLU score, outperforming published references for similar-sized models.
- Privacy-First: All computations run locally on the device.
- Compact Size: At 1.5B parameters, it's suitable for resource-constrained environments.
Benchmark Highlights (EXP-001)
Evaluated with a 5-shot prompting strategy, maxx-1-1.5B demonstrates strong performance:
- MMLU: 59.87%
- TruthfulQA: 45.99% (beating SmolLM2-1.7B by 6 points)
- Average: 57.75%, within 0.5% of larger competitors like Qwen2.5-1.5B-Instruct and SmolLM2-1.7B-Instruct.
Training Details
The model was fine-tuned using QLoRA (4-bit, rank 16) on a curated dataset of approximately 35,000 examples, including OpenHermes-2.5, UltraChat-200k, Glaive Function Calling v2, and Alpaca Cleaned. Synthetic data was also generated using Qwen2.5-7B as a teacher model. The training was a small run (200 steps) completed in about 1.5 hours on a Kaggle T4 GPU.
Intended Use Cases
- On-device AI assistants for offline task completion.
- Summarization, email drafting, scheduling, and planning.
- Agentic multi-step reasoning for everyday tasks.
Limitations
As an early experimental checkpoint (EXP-001), limitations include a small training run, limited safety alignment, and a context window of 2048 tokens. It has not yet been evaluated on coding tasks.