bolajiev/maxx-1-1.5B

maxx — On-Device Agentic LLM (1.5B)

A fine-tuned Qwen2.5-1.5B-Instruct model optimized for agentic tasks, instruction following, and real-world offline use on phones and laptops. First checkpoint in an ongoing research project targeting the best open-source agentic model under 3B parameters.

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Model Details

Field	Details
Base model	Qwen/Qwen2.5-1.5B-Instruct
Parameters	1.5B
Fine-tune method	QLoRA (4-bit, rank 16)
Framework	Unsloth + TRL
Context window	2048 tokens
License	Apache 2.0
Developer	bolajiev (Independent Researcher)
Status	EXP-001 — active research

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Benchmark Results (EXP-001)

Evaluated using lm-evaluation-harness with 5-shot prompting.

Benchmark	maxx (1.5B)	Qwen2.5-1.5B-Instruct	SmolLM2-1.7B-Instruct
ARC-Challenge ↑	52.47%	53.92%	51.88%
HellaSwag ↑	67.02%	67.71%	72.20%
WinoGrande ↑	65.51%	64.64%	68.98%
TruthfulQA ↑	45.99%	46.61%	39.96%
MMLU ↑	59.87%	—	—
Average	57.75%	58.22%	58.26%

Key findings:

• Within 0.5% of both larger/better-resourced competitors on first training run
• Beats SmolLM2-1.7B on TruthfulQA by +6 points — a bigger model
• MMLU of 59.87% outperforms published reference scores for both competitors
• Strong commonsense and knowledge base retained from Qwen2.5 foundation

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Intended Use

Primary use cases

• On-device AI assistant for phones and laptops (no internet required)
• Instruction following and task completion offline
• Summarization, email writing, scheduling, planning
• Agentic multi-step reasoning for everyday tasks
• Privacy-first AI — all compute runs locally

Out of scope

• High-stakes medical, legal, or financial decisions
• Tasks requiring real-time internet access
• Complex multi-modal tasks

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Training Details

Data

• OpenHermes-2.5 — instruction following
• UltraChat-200k — conversational quality
• Glaive Function Calling v2 — tool use and agentic tasks
• Alpaca Cleaned — general instructions
• Synthetic data generated via open-source teacher model (Qwen2.5-7B)

Total: ~35,000 curated examples (EXP-001 small run)

Hyperparameters

Parameter	Value
Learning rate	2e-4
Batch size	4
Gradient accumulation	4 (effective 16)
LoRA rank	16
LoRA alpha	32
Max steps	200
Optimizer	AdamW 8-bit
Scheduler	Cosine
Warmup steps	20

Hardware

• GPU: Kaggle T4 (16GB VRAM)
• Training time: ~1.5 hours
• Compute: ~3 GPU hours

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How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "bolajiev/maxx-1-1.5B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Write a short email to my boss saying I will be 10 minutes late."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=300, temperature=0.7, do_sample=True)

reply = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(reply)

On-Device with Ollama (GGUF)

# Use the quantized GGUF version for on-device inference
ollama run bolajiev/maxx-merged-gguf

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Limitations

• EXP-001 is a small training run (200 steps, ~35k examples) — not a final model
• Safety alignment is limited — some harmful requests may not be refused correctly
• Context window limited to 2048 tokens in this checkpoint
• Not evaluated on coding tasks yet
• HellaSwag gap vs SmolLM2 indicates commonsense reasoning can improve

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Built with Unsloth 🦥 | Trained on Kaggle T4