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Model: mlx-community/nesso-0.4B-agentic-mlx
Source: Original Platform

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README.md

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+---
+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)
+
+```
+

chat_template.jinja

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+{{ bos_token }}
+{% if tools %}
+<|im_start|>system
+{% if messages and messages[0]['role'] == 'system' %}
+{{ messages[0]['content'] }}
+{% endif %}
+# Tools
+You may call one or more functions to assist with the user query.
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{% for tool in tools %}
+{{ tool | tojson }}
+{% endfor %}
+</tools>
+For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
+<tool_call>
+{"name": <function-name>, "arguments": <args-json-object>}
+</tool_call><|im_end|>
+{% else %}
+{% if messages and messages[0]['role'] == 'system' %}
+<|im_start|>system
+{{ messages[0]['content'] }}<|im_end|>
+{% endif %}
+{% endif %}
+
+{% for message in messages %}
+{% if message.content is string %}
+  {% set content = message.content %}
+{% else %}
+  {% set content = '' %}
+{% endif %}
+
+{% if (message.role == 'user') or (message.role == 'system' and not loop.first) %}
+<|im_start|>{{ message.role }}
+{{ content }}<|im_end|>
+
+{% elif message.role == 'assistant' %}
+<|im_start|>assistant
+{{ content }}
+{% if message.tool_calls %}
+  {% for tool_call in message.tool_calls %}
+    {% if tool_call.function %}
+      {% set tool_call = tool_call.function %}
+    {% endif %}
+<tool_call>
+{"name": "{{ tool_call.name }}", "arguments": {% if tool_call.arguments is string %}{{ tool_call.arguments }}{% else %}{{ tool_call.arguments | tojson }}{% endif %}}
+</tool_call>
+  {% endfor %}
+{% endif %}
+<|im_end|>
+
+{% elif message.role == 'tool' %}
+{% if loop.first or (messages[loop.index0 - 1].role != 'tool') %}
+<|im_start|>user
+{% endif %}
+<tool_response>
+{{ content }}
+</tool_response>
+{% if loop.last or (messages[loop.index0 + 1].role != 'tool') %}
+<|im_end|>
+{% endif %}
+
+{% endif %}
+{% endfor %}
+
+{% if add_generation_prompt %}
+<|im_start|>assistant
+{% endif %}

config.json

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+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 128000,
+    "eos_token_id": 128256,
+    "head_dim": 64,
+    "hidden_act": "silu",
+    "hidden_size": 960,
+    "initializer_range": 0.02,
+    "intermediate_size": 2560,
+    "max_position_embeddings": 4096,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 15,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 5,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 10000.0,
+    "tie_word_embeddings": true,
+    "torch_dtype": "float32",
+    "transformers_version": "4.55.2",
+    "use_cache": false,
+    "vocab_size": 128262
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "transformers_version": "4.55.2"
+}

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