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Model: bolajiev/maxx-1-1.5B Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- qwen2
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- unsloth
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- trl
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- lora
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- on-device
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- agentic
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- offline
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- fine-tuned
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model_type: qwen2
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pipeline_tag: text-generation
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---
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# maxx — On-Device Agentic LLM (1.5B)
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> A fine-tuned Qwen2.5-1.5B-Instruct model optimized for agentic tasks,
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> instruction following, and real-world offline use on phones and laptops.
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> First checkpoint in an ongoing research project targeting the best
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> open-source agentic model under 3B parameters.
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---
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## Model Details
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| Field | Details |
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|---|---|
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| **Base model** | Qwen/Qwen2.5-1.5B-Instruct |
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| **Parameters** | 1.5B |
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| **Fine-tune method** | QLoRA (4-bit, rank 16) |
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| **Framework** | Unsloth + TRL |
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| **Context window** | 2048 tokens |
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| **License** | Apache 2.0 |
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| **Developer** | bolajiev (Independent Researcher) |
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| **Status** | EXP-001 — active research |
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---
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## Benchmark Results (EXP-001)
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Evaluated using [lm-evaluation-harness](https://github.com/EleutherAI/lm-harness) with 5-shot prompting.
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| Benchmark | maxx (1.5B) | Qwen2.5-1.5B-Instruct | SmolLM2-1.7B-Instruct |
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|---|---|---|---|
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| ARC-Challenge ↑ | 52.47% | **53.92%** | 51.88% |
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| HellaSwag ↑ | 67.02% | 67.71% | **72.20%** |
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| WinoGrande ↑ | 65.51% | 64.64% | **68.98%** |
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| TruthfulQA ↑ | 45.99% | 46.61% | 39.96% |
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| **MMLU ↑** | **59.87%** | — | — |
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| **Average** | **57.75%** | 58.22% | 58.26% |
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**Key findings:**
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- Within **0.5%** of both larger/better-resourced competitors on first training run
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- Beats SmolLM2-1.7B on TruthfulQA by **+6 points** — a bigger model
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- MMLU of **59.87%** outperforms published reference scores for both competitors
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- Strong commonsense and knowledge base retained from Qwen2.5 foundation
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---
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## Intended Use
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### Primary use cases
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- On-device AI assistant for phones and laptops (no internet required)
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- Instruction following and task completion offline
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- Summarization, email writing, scheduling, planning
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- Agentic multi-step reasoning for everyday tasks
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- Privacy-first AI — all compute runs locally
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### Out of scope
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- High-stakes medical, legal, or financial decisions
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- Tasks requiring real-time internet access
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- Complex multi-modal tasks
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---
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## Training Details
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### Data
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- OpenHermes-2.5 — instruction following
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- UltraChat-200k — conversational quality
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- Glaive Function Calling v2 — tool use and agentic tasks
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- Alpaca Cleaned — general instructions
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- Synthetic data generated via open-source teacher model (Qwen2.5-7B)
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**Total:** ~35,000 curated examples (EXP-001 small run)
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### Hyperparameters
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| Parameter | Value |
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|---|---|
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| Learning rate | 2e-4 |
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| Batch size | 4 |
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| Gradient accumulation | 4 (effective 16) |
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| LoRA rank | 16 |
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| LoRA alpha | 32 |
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| Max steps | 200 |
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| Optimizer | AdamW 8-bit |
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| Scheduler | Cosine |
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| Warmup steps | 20 |
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### Hardware
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- GPU: Kaggle T4 (16GB VRAM)
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- Training time: ~1.5 hours
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- Compute: ~3 GPU hours
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---
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## How to Use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "bolajiev/maxx-1-1.5B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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messages = [{"role": "user", "content": "Write a short email to my boss saying I will be 10 minutes late."}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(**inputs, max_new_tokens=300, temperature=0.7, do_sample=True)
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reply = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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print(reply)
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```
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### On-Device with Ollama (GGUF)
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```bash
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# Use the quantized GGUF version for on-device inference
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ollama run bolajiev/maxx-merged-gguf
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```
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---
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## Limitations
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- EXP-001 is a small training run (200 steps, ~35k examples) — not a final model
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- Safety alignment is limited — some harmful requests may not be refused correctly
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- Context window limited to 2048 tokens in this checkpoint
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- Not evaluated on coding tasks yet
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- HellaSwag gap vs SmolLM2 indicates commonsense reasoning can improve
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---
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---
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---
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*Built with [Unsloth](https://github.com/unslothai/unsloth) 🦥 | Trained on Kaggle T4*
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54
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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62
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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|
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|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"pad_token_id": 151665,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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"sliding_window": null,
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"tie_word_embeddings": true,
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"unsloth_fixed": true,
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"unsloth_version": "2026.5.8",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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generation_config.json
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|
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"repetition_penalty": 1.1,
|
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"temperature": 0.7,
|
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"top_k": 20,
|
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"top_p": 0.8,
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"transformers_version": "5.5.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9af2ca3bd59a17f6885f692f3c01deefdfc9220c7bf510affbdd591cb1af8292
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size 3087467144
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3
tokenizer.json
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3
tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
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size 11422356
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202
tokenizer_config.json
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tokenizer_config.json
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|
||||
"add_prefix_space": false,
|
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"backend": "tokenizers",
|
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|
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"clean_up_tokenization_spaces": false,
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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"151651": {
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|
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},
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"151652": {
|
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|
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},
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"151653": {
|
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"single_word": false,
|
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|
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"rstrip": false,
|
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"normalized": false,
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"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|PAD_TOKEN|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||
}
|
||||
Reference in New Issue
Block a user