README.md

---
license: apache-2.0
language:
  - en
  - de
base_model: Qwen/Qwen3-4B
tags:
  - mimi
  - tool-calling
  - function-calling
  - agent
  - gguf
  - fine-tuned
  - wllama
  - browser-inference
  - on-device-ai
  - local-ai
  - privacy-first
model-index:
  - name: MIMI Pro
    results:
      - task:
          type: function-calling
          name: Tool Calling
        dataset:
          type: gorilla-llm/Berkeley-Function-Calling-Leaderboard
          name: BFCL V4
        metrics:
          - type: accuracy
            value: 60.8
            name: Simple Function Calling (Python)
            verified: false
          - type: accuracy
            value: 57.5
            name: Multiple Sequential Calls
            verified: false
          - type: accuracy
            value: 90
            name: Irrelevance Detection
            verified: false
pipeline_tag: text-generation
---

# MIMI Pro

MIMI Pro is a 4-billion parameter AI agent model optimized for structured tool calling and autonomous task execution — designed to run entirely on-device, in the browser, with zero cloud dependencies.

Part of the MIMI Model Family by [Mimi Tech AI](https://mimitechai.com).

> **🔬 V1 — Experimental Release.** This model is fine-tuned for the MIMI Agent's custom tool-calling format. For standard tool calling, the base [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) may perform equally well or better with native `<tool_call>` prompting. V2 with official BFCL scores and Qwen3-native format support is in development.

## Performance

### BFCL V4 Benchmark (Partial — Single-Turn, 20 samples/category)

| Category | MIMI Pro V1 | Base Qwen3-4B | Notes |
|---|---|---|---|
| Simple Python | 60.8% (400 tests) | **80.0%** (20 tests) | Base outperforms |
| Simple Java | 21.0% (100 tests) | **60.0%** (20 tests) | Base outperforms |
| Multiple (Sequential) | 57.5% (200 tests) | **75.0%** (20 tests) | Base outperforms |
| Parallel | 2.0% (200 tests) | **75.0%** (20 tests) | Fine-tune degraded |
| Irrelevance | 90% (20 tests) | **100%** (20 tests) | Both strong |
| Live Simple | — | **90.0%** (20 tests) | Base only |

> ⚠️ **Important Context:** The previously reported "97.7% accuracy" was a **training validation metric** (token-level accuracy on the training/eval split), not a standardized benchmark score. The table above shows actual BFCL V4 results. We are working on a full official evaluation.

### Training Metrics (Internal)

| Metric | Value |
|---|---|
| Training Token Accuracy | 97.66% |
| Eval Token Accuracy | 97.29% |
| Training Loss | 0.084 |
| Parameters | 4.02 Billion |
| Quantized Size | 2.3 GB (Q4_K_M) |

## Architecture

MIMI Pro is built on [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B), fine-tuned with LoRA (rank=64, alpha=128) on 1,610 curated tool-calling examples using [Unsloth](https://github.com/unslothai/unsloth) on NVIDIA DGX Spark.

**Key Design Decisions:**
- Custom tool-calling format optimized for the MIMI Agent browser environment
- 19 tool types covering web search, code execution, file operations, browser automation
- Trained on NVIDIA DGX Spark (Grace Blackwell GB10, 128 GB unified memory)

**Known Limitations of V1:**
- Fine-tuning with aggressive hyperparameters (LoRA r=64, 3 epochs, LR 2e-4) caused some capability degradation vs. the base model, particularly for parallel tool calling
- The custom `{"tool": ..., "parameters": ...}` format diverges from Qwen3's native `<tool_call>` format
- V2 will address these issues with conservative fine-tuning and Qwen3-native format support

## Supported Tools

| Category | Tools |
|---|---|
| 🌐 Web | web_search, browse_url, browser_action |
| 💻 Code | execute_python, create_file, edit_file |
| 🔬 Research | deep_research, generate_document |
| 📁 System | read_file, list_directory, run_terminal |
| 🧠 Reasoning | Multi-step orchestration |

## Quick Start

### Browser (wllama/WebAssembly)

```javascript
import { Wllama } from '@anthropic-ai/wllama';

const wllama = new Wllama(wasmPaths);
await wllama.loadModelFromUrl(
  'https://huggingface.co/MimiTechAI/mimi-pro/resolve/main/mimi-qwen3-4b-q4km.gguf',
  { n_ctx: 4096 }
);

const response = await wllama.createChatCompletion([
  { role: 'system', content: 'You are MIMI, an AI agent with tool access.' },
  { role: 'user', content: 'Search for the latest AI news and summarize it' }
]);
```

### llama.cpp

```bash
./llama-cli -m mimi-qwen3-4b-q4km.gguf \
  -p "<|im_start|>system\nYou are MIMI, an AI agent with tool access.<|im_end|>\n<|im_start|>user\nSearch for the latest AI news<|im_end|>\n<|im_start|>assistant\n" \
  -n 512 --temp 0.6
```

### Python

```python
from llama_cpp import Llama
llm = Llama(model_path="mimi-qwen3-4b-q4km.gguf", n_ctx=4096)
output = llm.create_chat_completion(messages=[
    {"role": "system", "content": "You are MIMI, an AI agent with tool access."},
    {"role": "user", "content": "Search for the latest AI news"}
])
```

## Output Format

MIMI Pro V1 uses a custom format (V2 will support Qwen3-native `<tool_call>` format):

```json
{"tool": "web_search", "parameters": {"query": "latest AI news March 2026", "limit": 5}}
```

## The MIMI Model Family

| Model | Parameters | Size | Target Device | Status |
|---|---|---|---|---|
| MIMI Nano | 0.6B | ~400 MB | Any device, IoT | 🔜 Coming |
| MIMI Small | 1.7B | ~1.0 GB | Mobile & tablets | 🔜 Coming |
| **MIMI Pro** | **4.02B** | **2.3 GB** | **Desktop & laptop** | **✅ Available** |
| MIMI Max | 8B | ~4.5 GB | Workstations | 🔜 Coming |

All models share the same tool-calling format, are quantized to GGUF Q4_K_M, and run in the browser via WebAssembly.

## Training Details

```yaml
method: LoRA (PEFT) via Unsloth
base_model: Qwen/Qwen3-4B
lora_rank: 64
lora_alpha: 128
lora_dropout: 0.05
target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
learning_rate: 2.0e-04
epochs: 3
effective_batch_size: 8
max_seq_length: 2048
optimizer: adamw_8bit
precision: bf16
gradient_checkpointing: true
packing: true
dataset: 1,610 curated tool-calling examples (178K tokens)
hardware: NVIDIA DGX Spark (GB10 Grace Blackwell, 128 GB unified memory)
```

## Why MIMI?

- 🔒 **Privacy First** — Your data never leaves your device. Period.
- 💰 **Zero Cost** — No API keys, no subscriptions, no per-token billing.
- ⚡ **Fast** — Runs at native speed via WebAssembly, no server round-trips.
- 🌍 **Works Offline** — Once downloaded, no internet required.
- 🔧 **Tool Native** — Purpose-built for autonomous tool calling.

## Limitations

- V1 uses a custom tool-calling format (not Qwen3-native `<tool_call>`)
- Parallel tool calling (multiple simultaneous calls) is degraded vs. base model
- Context window: 4,096 tokens (training config). Base architecture supports 32K.
- Requires ~3 GB RAM for inference in browser.
- Q4_K_M quantization trades minimal quality for 3.5x size reduction.

## Roadmap

- [x] **V1** — Custom format, 19 tools, browser-optimized (current release)
- [ ] **V2** — Qwen3-native `<tool_call>` format, official BFCL V4 scores, conservative fine-tuning
- [ ] **Model Family** — Nano (0.6B), Small (1.7B), Max (8B) releases
- [ ] **Multi-Turn** — Agentic conversation chains with tool result feedback

## About Mimi Tech AI

[Mimi Tech AI](https://mimitechai.com) builds on-device AI — no cloud, no data leaks, full user control.

- 🌐 [mimitechai.com](https://mimitechai.com)
- 🐙 [GitHub](https://github.com/MimiTechAi)
- 💼 [LinkedIn](https://linkedin.com/company/mimitechai)
- 🟢 [NVIDIA Connect Program](https://www.nvidia.com/en-us/industries/nvidia-connect-program/) Member

## License

Apache 2.0 — free for commercial and personal use.

## Citation

```bibtex
@misc{mimitechai2026mimi,
  title={MIMI Pro: On-Device AI Agent Model for Browser-Based Tool Calling},
  author={Bemler, Michael and Soppa, Michael},
  year={2026},
  publisher={Mimi Tech AI},
  url={https://huggingface.co/MimiTechAI/mimi-pro}
}
```