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Model: mii-llm/nesso-0.4B-agentic Source: Original Platform
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---
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language:
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- it
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- en
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license: apache-2.0
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tags:
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- small-language-model
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- slm
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- edge-ai
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- italian
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- bilingual
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- function-calling
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- agentic
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- structured-output
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- tool-use
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- llama
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- nanotron
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- axolotl
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base_model: mii-llm/zagreus-0.4B-ita
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model_type: llama
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Nesso-0.4B-Agentic
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**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.
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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.
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> ⚠️ 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.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| **Architecture** | Modified Llama-3.2 (fully dense) |
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| **Parameters** | ~400M |
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| **Hidden size** | 960 |
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| **Layers** | 32 |
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| **Attention heads** | 15 (KV heads: 5) |
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| **Context length** | 4096 tokens |
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| **Tokenizer** | Llama-3.2 (`vocab_size`: 128,256) |
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| **Precision** | BF16 |
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| **Languages** | English, Italian |
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| **Base model** | mii-llm/zagreus-0.4B-ita |
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| **Post-training framework** | Axolotl + FSDP |
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| **Chat template** | ChatML |
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---
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## Training Details
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### Base Model Pre-training
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`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:
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| Dataset | Description |
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|---|---|
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| [FineWeb (350BT sample)](https://huggingface.co/datasets/HuggingFaceFW/fineweb/viewer/sample-350BT) | ~350B tokens of English web text |
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| [FineWeb-2 (ita_Latn)](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2/viewer/ita_Latn) | Italian web text |
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| [FinePDFs (ita_Latn)](https://huggingface.co/datasets/HuggingFaceFW/finepdfs/viewer/ita_Latn) | Italian PDF documents |
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| [StarCoder Data](https://huggingface.co/datasets/bigcode/starcoderdata) | ~250B tokens of code |
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**Token distribution**: ~400B English + ~400B Italian + ~200B Code
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**Infrastructure**: 64× NVIDIA A100 GPUs (8 nodes × 8 GPUs) on Seeweb HPC
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**Framework**: [Nanotron (mii-llm fork)](https://github.com/mii-llm/nanotron)
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### Post-training (SFT)
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Post-training was performed using **Axolotl** with FSDP across 4 nodes (32× A100 GPUs).
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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.
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**Key hyperparameters:**
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| Hyperparameter | Value |
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|---|---|
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| Optimizer | AdamW (fused) |
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| Learning rate | `1e-3` |
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| LR scheduler | Cosine (constant ratio: 0.8, min ratio: 0.3) |
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| Epochs | 3 |
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| Micro batch size | 1 |
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| Gradient accumulation steps | 8 |
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| Sequence length | 4096 |
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| Max grad norm | 1.0 |
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| Precision | BF16 + Flash Attention |
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| FSDP strategy | FULL_SHARD |
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---
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## Chat Template
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This model uses the **ChatML** format:
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```
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<|im_start|>system
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You are a helpful assistant with access to tools.<|im_end|>
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<|im_start|>user
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What is the weather in Rome today?<|im_end|>
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<|im_start|>assistant
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```
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Special tokens:
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- `pad_token`: `<|im_end|>`
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- `eos_token`: `<|im_end|>`
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---
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "mii-llm/nesso-0.4B-agentic"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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import re
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def chat(messages, tools=None, max_tokens=256):
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prompt = tokenizer.apply_chat_template(
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messages,
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tools=tools,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=max_tokens,
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do_sample=False,
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temperature=0.5,
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top_p=1.0,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id,
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)
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text = tokenizer.decode(outputs[0], skip_special_tokens=False)
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blocks = re.findall(
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r"<\|im_start\|>assistant\s*(.*?)<\|im_end\|>",
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text,
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flags=re.S
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)
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answer = blocks[-1].strip() if blocks else text.strip()
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print("\n=== RAW OUTPUT ===\n")
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print(text)
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print("\n=== PARSED ASSISTANT ===\n")
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print(answer)
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return answer
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system_prompt = (
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"Sei un assistente che può usare strumenti.\n"
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"Quando servono informazioni esterne, chiama una funzione.\n"
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"Usa ESATTAMENTE il formato <tool_call> previsto."
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)
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# ----- TOOL DEFINITIONS -----
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_weather",
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"description": "Ritorna il meteo per una città",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {"type": "string"}
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},
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"required": ["city"]
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}
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}
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}
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]
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# ----- MESSAGES -----
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": "Che tempo fa a Milano?"}
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]
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out = chat(messages, tools=tools)
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```
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> 💡 **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.
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---
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## Evaluation
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We used our [fork of lm-evaluation-harness](https://github.com/mii-llm/lm-evaluation-harness/) for multilingual
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### Evaluation Commands
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```bash
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# Italian benchmarks
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lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
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--tasks m_mmlu_it --num_fewshot 5 --device cuda:0 --batch_size 1
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lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
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--tasks hellaswag_it,arc_it --device cuda:0 --batch_size 1
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lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
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--tasks ifeval-ita --device cuda:0 --batch_size 1
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# English benchmarks
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lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
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--tasks mmlu --num_fewshot 5 --device cuda:0 --batch_size 1
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lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
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--tasks hellaswag,arc --device cuda:0 --batch_size 1
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lm-eval --model hf --model_args pretrained=mii-llm/nesso-0.4B-agentic \
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--tasks ifeval --device cuda:0 --batch_size 1
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```
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### Results
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#### English Benchmarks
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| Model | IFEval EN ↑ | ARC EN ↑ | HellaSwag EN ↑ | MMLU EN ↑ | **Avg EN** |
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|---|---|---|---|---|---|
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| Qwen/Qwen3-0.6B | 0.2758 | 0.3430 | **0.4742** | **0.4013** | **0.3736** |
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| Nesso-0.4B-instruct | **0.3465** | **0.3003** | 0.4629 | 0.2871 | 0.3492 |
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| **Nesso-0.4B-agentic** | 0.2962 | 0.2534 | 0.4062 | 0.2889 | 0.3112 |
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| LiquidAI/LFM2-350M | 0.1595 | 0.2457 | 0.3092 | 0.3445 | 0.2647 |
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#### Italian Benchmarks
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| Model | IFEval IT ↑ | ARC IT ↑ | HellaSwag IT ↑ | MMLU IT ↑ | **Avg IT** |
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|---|---|---|---|---|---|
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| Qwen/Qwen3-0.6B | **0.3058** | 0.2729 | 0.3598 | **0.4025** | **0.3353** |
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| Nesso-0.4B-instruct | 0.2962 | **0.2874** | **0.4076** | 0.2875 | 0.3197 |
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| **Nesso-0.4B-agentic** | 0.2914 | 0.2541 | 0.3673 | 0.2730 | 0.2965 |
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| LiquidAI/LFM2-350M | 0.1427 | 0.2464 | 0.2994 | 0.3132 | 0.2504 |
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#### Overall
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| Model | Avg EN | Avg IT | **Overall** |
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|---|---|---|---|
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| Qwen/Qwen3-0.6B | 0.3736 | 0.3353 | 0.3545 |
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| Nesso-0.4B-instruct | 0.3492 | 0.3197 | 0.3345 |
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| **Nesso-0.4B-agentic** | 0.3112 | 0.2965 | **0.3039** |
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| LiquidAI/LFM2-350M | 0.2647 | 0.2504 | 0.2576 |
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### Discussion
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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.
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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.
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---
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## Related Models
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| Model | Description |
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|---|---|
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| [Zagreus-0.4B-ita](https://huggingface.co/mii-llm/zagreus-0.4B-ita) | Base pre-trained model (this model's foundation) |
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| [Nesso-0.4B-instruct](https://huggingface.co/mii-llm/nesso-0.4B-instruct) | Optimized for conversational and instruction-following tasks |
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| [Open-Zagreus-0.4B](https://huggingface.co/mii-llm/open-zagreus-0.4B) | Fully open-source SFT variant |
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---
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## Citation
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If you use this model in your research, please cite:
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```bibtex
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@misc{nesso2025,
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title = {The Joy and Pain of Training an LLM from Scratch:
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A Technical Report on the Zagreus and Nesso Model Families},
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author = {mii-llm community},
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year = {2025},
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howpublished = {\url{https://github.com/mii-llm/zagreus-nesso-slm}},
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}
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```
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---
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## Acknowledgements
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- **Antonio Baldassarra** (CEO, Seeweb) and **Marco Cristofanilli** (Head of AI, Seeweb) for infrastructure sponsorship
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- The **Hugging Face** team for Nanotron, datatrove, FineWeb, and FineWeb-2
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- The **mii-llm** open-source community
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---
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|
||||||
|
## License
|
||||||
|
|
||||||
|
Released under the **Apache 2.0** license.
|
||||||
|
|
||||||
|
> Made with ❤️ in Italy by [mii-llm](https://mii-llm.ai)
|
||||||
68
chat_template.jinja
Normal file
68
chat_template.jinja
Normal file
@@ -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 <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 %}
|
||||||
29
config.json
Normal file
29
config.json
Normal file
@@ -0,0 +1,29 @@
|
|||||||
|
{
|
||||||
|
"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
|
||||||
|
}
|
||||||
7
generation_config.json
Normal file
7
generation_config.json
Normal file
@@ -0,0 +1,7 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"bos_token_id": 128000,
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": 128001,
|
||||||
|
"transformers_version": "4.55.2"
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:1aad710a5b7b714af028343de781f207eb314d1c8f781f4abbfbf7b2b4b3216d
|
||||||
|
size 2243626016
|
||||||
67
special_tokens_map.json
Normal file
67
special_tokens_map.json
Normal file
@@ -0,0 +1,67 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
{
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"content": "<tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"content": "</tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"content": "<tool_response>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"content": "</tool_response>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|begin_of_text|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|end_of_text|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:4a80502037c38583156839ad1486db1e7e5aa0b3f9d47e072f16e6b30a0eb2dd
|
||||||
|
size 17211058
|
||||||
2119
tokenizer_config.json
Normal file
2119
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user