commit 58eb04bac9302d836a1214ae3d26e1605d98efe4 Author: ModelHub XC Date: Mon May 25 10:03:16 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: mii-llm/nesso-0.4B-agentic Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..3be8176 --- /dev/null +++ b/README.md @@ -0,0 +1,307 @@ +--- +language: +- it +- en +license: apache-2.0 +tags: +- small-language-model +- slm +- edge-ai +- italian +- bilingual +- function-calling +- agentic +- structured-output +- tool-use +- llama +- nanotron +- axolotl +base_model: mii-llm/zagreus-0.4B-ita +model_type: llama +pipeline_tag: text-generation +library_name: transformers +--- + +# Nesso-0.4B-Agentic + +**Nesso-0.4B-Agentic** 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) | +| **Precision** | BF16 | +| **Languages** | English, Italian | +| **Base model** | mii-llm/zagreus-0.4B-ita | +| **Post-training framework** | Axolotl + FSDP | +| **Chat template** | ChatML | + +--- + +## 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 | + +--- + +## 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 + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM +import torch + +model_id = "mii-llm/nesso-0.4B-agentic" + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.bfloat16, + device_map="auto" +) + + +import re + +def chat(messages, tools=None, max_tokens=256): + prompt = tokenizer.apply_chat_template( + messages, + tools=tools, + tokenize=False, + add_generation_prompt=True + ) + + inputs = tokenizer(prompt, return_tensors="pt").to(model.device) + + outputs = model.generate( + **inputs, + max_new_tokens=max_tokens, + do_sample=False, + temperature=0.5, + top_p=1.0, + eos_token_id=tokenizer.eos_token_id, + pad_token_id=tokenizer.eos_token_id, + ) + + text = tokenizer.decode(outputs[0], skip_special_tokens=False) + + blocks = re.findall( + r"<\|im_start\|>assistant\s*(.*?)<\|im_end\|>", + text, + flags=re.S + ) + + answer = blocks[-1].strip() if blocks else text.strip() + + print("\n=== RAW OUTPUT ===\n") + print(text) + print("\n=== PARSED ASSISTANT ===\n") + print(answer) + + return answer + +system_prompt = ( + "Sei un assistente che può usare strumenti.\n" + "Quando servono informazioni esterne, chiama una funzione.\n" + "Usa ESATTAMENTE il formato previsto." +) + +# ----- TOOL DEFINITIONS ----- +tools = [ + { + "type": "function", + "function": { + "name": "get_weather", + "description": "Ritorna il meteo per una città", + "parameters": { + "type": "object", + "properties": { + "city": {"type": "string"} + }, + "required": ["city"] + } + } + } +] + +# ----- MESSAGES ----- +messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": "Che tempo fa a Milano?"} +] + +out = chat(messages, tools=tools) +``` + +> 💡 **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. + +--- + +## 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}}, +} +``` + +--- + +## 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) \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..e129b5d --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,68 @@ +{{ 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 XML tags: + +{% for tool in tools %} +{{ tool | tojson }} +{% endfor %} + +For each function call, return a json object with function name and arguments within XML tags: + +{"name": , "arguments": } +<|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 %} + +{"name": "{{ tool_call.name }}", "arguments": {% if tool_call.arguments is string %}{{ tool_call.arguments }}{% else %}{{ tool_call.arguments | tojson }}{% endif %}} + + {% endfor %} +{% endif %} +<|im_end|> + +{% elif message.role == 'tool' %} +{% if loop.first or 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