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Model: bolajiev/maxx-1-1.5B
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+tags:
+- qwen2
+- unsloth
+- trl
+- lora
+- on-device
+- agentic
+- offline
+- fine-tuned
+model_type: qwen2
+pipeline_tag: text-generation
+---
+
+# 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.
+
+---
+
+## 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 |
+
+---
+
+## Benchmark Results (EXP-001)
+
+Evaluated using [lm-evaluation-harness](https://github.com/EleutherAI/lm-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
+
+---
+
+## 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
+
+---
+
+## 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
+
+---
+
+## How to Use
+
+```python
+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)
+```bash
+# Use the quantized GGUF version for on-device inference
+ollama run bolajiev/maxx-merged-gguf
+```
+
+---
+
+## 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
+
+---
+
+---
+
+
+---
+
+*Built with [Unsloth](https://github.com/unslothai/unsloth) 🦥 | Trained on Kaggle T4*

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": null,
+    "torch_dtype": "float16",
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 1536,
+    "initializer_range": 0.02,
+    "intermediate_size": 8960,
+    "layer_types": [
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 32768,
+    "max_window_layers": 21,
+    "model_type": "qwen2",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 2,
+    "pad_token_id": 151665,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000.0,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.5.8",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 32768,
+  "pad_token_id": 151665,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.5.0"
+}

model.safetensors

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+oid sha256:9af2ca3bd59a17f6885f692f3c01deefdfc9220c7bf510affbdd591cb1af8292
+size 3087467144

tokenizer.json

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+oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
+size 11422356

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 32768,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "added_tokens_decoder": {
+    "151643": {
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+      "lstrip": false,
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+      "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"
+}