---
language:
- it
- en
license: apache-2.0
tags:
- small-language-model
- slm
- edge-ai
- italian
- bilingual
- function-calling
- agentic
- structured-output
- tool-use
- llama
- mlx
base_model: mii-llm/nesso-0.4B-agentic
model_type: llama
pipeline_tag: text-generation
library_name: mlx
---

# Nesso-0.4B-Agentic-MLX

**Nesso-0.4B-Agentic-MLX** is the Apple Silicon-optimized version of [Nesso-0.4B-Agentic](https://huggingface.co/mii-llm/nesso-0.4B-agentic). It has been converted to the MLX format for high-performance inference on Mac M-series chips.

It is a bilingual English/Italian Small Language Model (SLM) optimized for **function calling, structured output generation, and agentic execution patterns**. It is post-trained on top of [Zagreus-0.4B-ita](https://huggingface.co/mii-llm/zagreus-0.4B-ita), a foundational model trained from scratch by the [mii-llm](https://mii-llm.ai) community (*Made in Italy – Large Language Model*) on the [Seeweb](https://www.seeweb.it) HPC infrastructure.

Designed for **sovereign edge inference**, Nesso-0.4B-Agentic targets deployment scenarios that require reliable tool use, structured JSON output, and multi-step agentic reasoning — all within a compact ~400M parameter footprint.

> ⚠️ This model is currently at the **SFT (Supervised Fine-Tuning)** stage. DPO (Direct Preference Optimization) training is planned and updated results will be published upon completion.

---

## Model Details

| Property | Value |
|---|---|
| **Architecture** | Modified Llama-3.2 (fully dense) |
| **Parameters** | ~400M |
| **Hidden size** | 960 |
| **Layers** | 32 |
| **Attention heads** | 15 (KV heads: 5) |
| **Context length** | 4096 tokens |
| **Tokenizer** | Llama-3.2 (`vocab_size`: 128,256) |
| **Format** | MLX |
| **Languages** | English, Italian |
| **Base model** | mii-llm/nesso-0.4B-agentic |
| **Post-training framework** | Axolotl + FSDP |
| **Chat template** | ChatML |

---

## Chat Template

This model uses the **ChatML** format:


```

<|im_start|>system
You are a helpful assistant with access to tools.<|im_end|>
<|im_start|>user
What is the weather in Rome today?<|im_end|>
<|im_start|>assistant

```

Special tokens:
- `pad_token`: `<|im_end|>`
- `eos_token`: `<|im_end|>`

---

## Usage

### Installation

```bash
pip install mlx-lm

```

### Inference via Python

```python
from mlx_lm import load, generate

model_id = "mlx-community/nesso-0.4B-agentic-mlx" 

model, tokenizer = load(model_id)

system_prompt = (
    "Sei un assistente che può usare strumenti.\n"
    "Quando servono informazioni esterne, chiama una funzione.\n"
    "Usa ESATTAMENTE il formato <tool_call> previsto."
)

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Che tempo fa a Milano?"}
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

response = generate(model, tokenizer, prompt=prompt, verbose=True, temp=0.3, max_tokens=256)
print(response)

```

### Inference via Terminal

```bash
python -m mlx_lm.generate --model mlx-community/nesso-0.4B-agentic-mlx \
  --prompt "<|im_start|>system\nSei un assistente che può usare strumenti.<|im_end|>\n<|im_start|>user\nChe tempo fa a Milano?<|im_end|>\n<|im_start|>assistant\n" \
  --temp 0.3 --max-tokens 256

```

> 💡 **Tip**: For function calling and structured output tasks, we recommend using a lower temperature (`0.1`–`0.3`) to improve JSON validity and output consistency.

---

## Training Details

### Base Model Pre-training

`Nesso-0.4B-Agentic` is built on `Zagreus-0.4B-ita`, which was pre-trained on approximately **1 trillion tokens** using the following data mix:

| Dataset | Description |
| --- | --- |
| [FineWeb (350BT sample)](https://huggingface.co/datasets/HuggingFaceFW/fineweb/viewer/sample-350BT) | ~350B tokens of English web text |
| [FineWeb-2 (ita_Latn)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2/viewer/ita_Latn) | Italian web text |
| [FinePDFs (ita_Latn)](https://huggingface.co/datasets/HuggingFaceFW/finepdfs/viewer/ita_Latn) | Italian PDF documents |
| [StarCoder Data](https://huggingface.co/datasets/bigcode/starcoderdata) | ~250B tokens of code |

**Token distribution**: ~400B English + ~400B Italian + ~200B Code

**Infrastructure**: 64× NVIDIA A100 GPUs (8 nodes × 8 GPUs) on Seeweb HPC

**Framework**: [Nanotron (mii-llm fork)](https://github.com/mii-llm/nanotron)

### Post-training (SFT)

Post-training was performed using **Axolotl** with FSDP across 4 nodes (32× A100 GPUs).

The instruction dataset is a **proprietary bilingual (English/Italian)** corpus curated by the mii-llm team, with dedicated focus on **function calling, structured JSON output, tool orchestration, and agentic execution patterns**. This dataset was built through years of iteration across domains including finance, cybersecurity, and multi-step agentic workflows, and is considered a strategic research asset not released as open source.

**Key hyperparameters:**

| Hyperparameter | Value |
| --- | --- |
| Optimizer | AdamW (fused) |
| Learning rate | `1e-3` |
| LR scheduler | Cosine (constant ratio: 0.8, min ratio: 0.3) |
| Epochs | 3 |
| Micro batch size | 1 |
| Gradient accumulation steps | 8 |
| Sequence length | 4096 |
| Max grad norm | 1.0 |
| Precision | BF16 + Flash Attention |
| FSDP strategy | FULL_SHARD |

---

## Evaluation

We used our [fork of lm-evaluation-harness](https://github.com/mii-llm/lm-evaluation-harness/) for multilingual

### Evaluation Commands

```bash
# Italian benchmarks
lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
  --tasks m_mmlu_it --num_fewshot 5 --device cuda:0 --batch_size 1

lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
  --tasks hellaswag_it,arc_it --device cuda:0 --batch_size 1

lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
  --tasks ifeval-ita --device cuda:0 --batch_size 1

# English benchmarks
lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
  --tasks mmlu --num_fewshot 5 --device cuda:0 --batch_size 1

lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
  --tasks hellaswag,arc --device cuda:0 --batch_size 1

lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
  --tasks ifeval --device cuda:0 --batch_size 1

```

### Results

#### English Benchmarks

| Model | IFEval EN ↑ | ARC EN ↑ | HellaSwag EN ↑ | MMLU EN ↑ | **Avg EN** |
| --- | --- | --- | --- | --- | --- |
| Qwen/Qwen3-0.6B | 0.2758 | 0.3430 | **0.4742** | **0.4013** | **0.3736** |
| Nesso-0.4B-instruct | **0.3465** | **0.3003** | 0.4629 | 0.2871 | 0.3492 |
| **Nesso-0.4B-agentic** | 0.2962 | 0.2534 | 0.4062 | 0.2889 | 0.3112 |
| LiquidAI/LFM2-350M | 0.1595 | 0.2457 | 0.3092 | 0.3445 | 0.2647 |

#### Italian Benchmarks

| Model | IFEval IT ↑ | ARC IT ↑ | HellaSwag IT ↑ | MMLU IT ↑ | **Avg IT** |
| --- | --- | --- | --- | --- | --- |
| Qwen/Qwen3-0.6B | **0.3058** | 0.2729 | 0.3598 | **0.4025** | **0.3353** |
| Nesso-0.4B-instruct | 0.2962 | **0.2874** | **0.4076** | 0.2875 | 0.3197 |
| **Nesso-0.4B-agentic** | 0.2914 | 0.2541 | 0.3673 | 0.2730 | 0.2965 |
| LiquidAI/LFM2-350M | 0.1427 | 0.2464 | 0.2994 | 0.3132 | 0.2504 |

#### Overall

| Model | Avg EN | Avg IT | **Overall** |
| --- | --- | --- | --- |
| Qwen/Qwen3-0.6B | 0.3736 | 0.3353 | 0.3545 |
| Nesso-0.4B-instruct | 0.3492 | 0.3197 | 0.3345 |
| **Nesso-0.4B-agentic** | 0.3112 | 0.2965 | **0.3039** |
| LiquidAI/LFM2-350M | 0.2647 | 0.2504 | 0.2576 |

### Discussion

Nesso-0.4B-Agentic is trained with a specialization trade-off: its post-training data prioritizes **structured output fidelity, tool calling accuracy, and agentic planning** over general benchmark performance. As a result, scores on standard academic benchmarks (IFEval, MMLU, ARC) are lower than the instruct variant, which is expected behavior for a task-specialized model.

Nesso-0.4B-Agentic still **outperforms LiquidAI/LFM2-350M across all benchmarks** in both languages, confirming its quality as a competitive small model. Its real-world advantage over general-purpose models of similar size is best assessed on agentic and function-calling tasks rather than academic benchmarks.

---

## Related Models

| Model | Description |
| --- | --- |
| [Zagreus-0.4B-ita](https://huggingface.co/mii-llm/zagreus-0.4B-ita) | Base pre-trained model (this model's foundation) |
| [Nesso-0.4B-instruct](https://huggingface.co/mii-llm/nesso-0.4B-instruct) | Optimized for conversational and instruction-following tasks |
| [Open-Zagreus-0.4B](https://huggingface.co/mii-llm/open-zagreus-0.4B) | Fully open-source SFT variant |

---

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{nesso2025,
  title        = {The Joy and Pain of Training an LLM from Scratch:
                  A Technical Report on the Zagreus and Nesso Model Families},
  author       = {mii-llm community},
  year         = {2025},
  howpublished = {\url{[https://github.com/mii-llm/zagreus-nesso-slm](https://github.com/mii-llm/zagreus-nesso-slm)}},
}

```

---

## Acknowledgements

* **Antonio Baldassarra** (CEO, Seeweb) and **Marco Cristofanilli** (Head of AI, Seeweb) for infrastructure sponsorship
* The **Hugging Face** team for Nanotron, datatrove, FineWeb, and FineWeb-2
* The **mii-llm** open-source community

---

## License

Released under the **Apache 2.0** license.

> Made with ❤️ in Italy by [mii-llm](https://mii-llm.ai)

```