commit 8d27fbb56b6bf913ca928a67e7e129d2ae38e147 Author: ModelHub XC Date: Sat May 30 17:08:19 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: mii-llm/zagreus-0.4B-ita 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..25de489 --- /dev/null +++ b/README.md @@ -0,0 +1,412 @@ +--- +language: +- it +- en +license: apache-2.0 +tags: +- small-language-model +- slm +- edge-ai +- italian +- bilingual +- base-model +- pretrained +- llama +- nanotron +- from-scratch +model_type: llama +pipeline_tag: text-generation +library_name: transformers +datasets: +- HuggingFaceFW/fineweb +- HuggingFaceFW/fineweb-2 +- HuggingFaceFW/finepdfs +- bigcode/starcoderdata +--- + +# Zagreus-0.4B-ita + +**Zagreus-0.4B-ita** is a bilingual English/Italian foundational Small Language Model (SLM) 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. + +This is a **base (pre-trained) model** — it is not instruction-tuned and is intended for researchers, developers, and practitioners who want to fine-tune or build upon a high-quality bilingual English/Italian foundation. It serves as the base for the entire [Nesso model family](https://huggingface.co/mii-llm). + +The Zagreus family represents one of the few openly released, high-performing small language models dedicated to European Romance languages, trained entirely from first principles with a fully transparent pipeline. + +--- + +## Model Details + +| Property | Value | +|---|---| +| **Architecture** | Modified Llama-3.2 (fully dense) | +| **Parameters** | ~400M | +| **Hidden size** | 960 | +| **Intermediate size** | 2560 | +| **Layers** | 32 | +| **Attention heads** | 15 (KV heads: 5) | +| **Activation** | SiLU | +| **Context length** | 4096 tokens | +| **Tokenizer** | Llama-3.2 (`vocab_size`: 128,256) | +| **Positional encoding** | RoPE (`theta`: 10000.0) | +| **Tied embeddings** | Yes | +| **Precision** | BF16 | +| **Languages** | English (~400B tokens), Italian (~400B tokens) | +| **Training tokens** | ~1 trillion | +| **Training framework** | [Nanotron (mii-llm fork)](https://github.com/mii-llm/nanotron) | +| **Infrastructure** | 64× NVIDIA A100 GPUs (8 nodes × 8 GPUs), Seeweb HPC | + +--- + +## Training Data + +All datasets used are fully open source and released by Hugging Face: + +| Dataset | Tokens | Description | +|---|---|---| +| [FineWeb (350BT sample)](https://huggingface.co/datasets/HuggingFaceFW/fineweb/viewer/sample-350BT) | ~350B | High-quality 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 | Multilingual code | + +**Token distribution**: ~400B English + ~400B Italian + ~200B Code ≈ **1 trillion tokens total** + +### Tokenization + +Raw datasets were tokenized using the **Llama-3.2 tokenizer** (`meta-llama/Llama-3.2-1B`) via the [datatrove](https://github.com/huggingface/datatrove) library. The process ran for over **three weeks of continuous computation** on CPU nodes via Slurm, generating approximately 3–5 TB of tokenized data shards. + +--- + +## Architecture Choice + +We adopted a **modified Llama-3.2 fully dense architecture**. The choice of a dense model over Mixture-of-Experts (MoE) in the small-parameter regime (~500M) was deliberate: in tightly constrained capacity settings, routing overhead and expert under-utilization typical of MoE architectures may offset their theoretical efficiency advantages. Dense models provide better compute utilization and more stable training dynamics at this scale. + +--- + +## Pre-training Configuration + +Full Nanotron YAML configuration used for training: + +```yaml +checkpoints: + checkpoint_interval: 5000 + checkpoints_path: checkpoints_zagreus_ita_v2 + checkpoints_path_is_shared_file_system: false + resume_checkpoint_path: /training/pretraining/nanotron/checkpoints_zagreus_ita_v2/630000 + save_final_state: false + save_initial_state: false +data_stages: +- data: + dataset: + dataset_folder: + - /training/pretraining/fineweb-ita/tokenized + - /training/pretraining/fineweb-edu-350BT/000_tokenized_output + - /training/pretraining/fineweb-edu-350BT/011_tokenized_output + - /training/pretraining/fineweb-edu-350BT/012_tokenized_output + - /training/pretraining/fineweb-edu-350BT/013_tokenized_output + - /training/pretraining/fineweb-edu-350BT/014_tokenized_output + - /training/pretraining/fineweb-edu-350BT/015_tokenized_output + - /training/pretraining/fineweb-edu-350BT/016_tokenized_output + - /training/pretraining/finepdf-ita/000_tokenized_output + - /training/pretraining/starcoder_tokenized/000_tokenized_output + num_loading_workers: 0 + seed: 8 + name: stable phase + start_training_step: 1 +general: + benchmark_csv_path: null + consumed_train_samples: null + ignore_sanity_checks: true + project: zagreus + run: zagreus-350M + seed: 8 + step: null +logging: + iteration_step_info_interval: 1 + log_level: info + log_level_replica: info +model: + ddp_bucket_cap_mb: 100 + dtype: bfloat16 + init_method: + std: 0.03227 + make_vocab_size_divisible_by: 1 + model_config: + bos_token_id: 128000 + eos_token_id: 128001 + hidden_act: silu + hidden_size: 960 + initializer_range: 0.02 + intermediate_size: 2560 + is_llama_config: true + max_position_embeddings: 4096 + num_attention_heads: 15 + num_hidden_layers: 32 + num_key_value_heads: 5 + pad_token_id: null + pretraining_tp: 1 + rms_norm_eps: 1.0e-05 + rope_interleaved: false + rope_scaling: null + rope_theta: 10000.0 + tie_word_embeddings: true + use_cache: true + vocab_size: 128256 +optimizer: + accumulate_grad_in_fp32: true + clip_grad: 1.0 + learning_rate_scheduler: + learning_rate: 0.003 + lr_decay_starting_step: 750000 + lr_decay_steps: 50000 + lr_decay_style: linear + lr_warmup_steps: 4000 + lr_warmup_style: linear + min_decay_lr: 1.0e-7 + optimizer_factory: + adam_beta1: 0.9 + adam_beta2: 0.95 + adam_eps: 1.0e-08 + name: adamW + torch_adam_is_fused: true + weight_decay: 0.01 + zero_stage: 0 +parallelism: + dp: 64 + expert_parallel_size: 1 + pp: 1 + pp_engine: 1f1b + recompute_layer: false + tp: 1 + tp_linear_async_communication: true + tp_mode: REDUCE_SCATTER + tp_recompute_allgather: true +profiler: null +tokenizer: + tokenizer_max_length: null + tokenizer_name_or_path: meta-llama/Llama-3.2-1B + tokenizer_revision: null +tokens: + batch_accumulation_per_replica: 1 + limit_test_batches: 0 + limit_val_batches: 0 + micro_batch_size: 4 + sequence_length: 4096 + train_steps: 2000000 + val_check_interval: 5000 +``` + +### Slurm Launch Script + +```bash +#SBATCH --job-name=350_it +#SBATCH --account=YOUR_ACCOUNT +#SBATCH --partition=PARTITION +#SBATCH --nodes=8 +#SBATCH --gres=gpu:8 # 8 A100 per node = 64 total +#SBATCH --cpus-per-task=32 +#SBATCH --time=4-00:00:00 +#SBATCH --output=slurm-%j.out + +################ 0. Environment ################ +module purge +module load profile/global +module load python/3.11 cuda/12.2 cudnn nccl gcc + +source /path/to/venv/nanotron/bin/activate + +export HF_HOME=/path/to/hf_home +export TRANSFORMERS_OFFLINE=1 +export HF_HUB_OFFLINE=1 +export HF_DATASETS_OFFLINE=1 +export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK +export NCCL_IB_DISABLE=0 +export NCCL_SOCKET_IFNAME="ib0,eno,eth" +export WANDB_MODE=disabled + +################ 1. Distributed vars ############ +GPUS_PER_NODE=4 +NNODES=$SLURM_JOB_NUM_NODES +NODE_RANK=$SLURM_NODEID +MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n1) +MASTER_PORT=29400 +RDZV_ID=$SLURM_JOB_ID + +################ 2. Launch ###################### +srun torchrun \ + --nnodes $NNODES \ + --nproc_per_node $GPUS_PER_NODE \ + --rdzv_id $RDZV_ID \ + --rdzv_backend c10d \ + --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT \ + /path/to/nanotron/run_train.py \ + --config-file smollm2/zagreus_350M_ita.yaml +``` + +### Checkpoint Conversion to Hugging Face Format + +```bash +torchrun --nproc_per_node=1 -m examples.llama.convert_nanotron_to_hf \ + --checkpoint_path=checkpoints/544000 \ + --save_path=hf_checkpoints/544000 \ + --tokenizer_name meta-llama/Llama-3.2-1B +``` + +--- + +## Evaluation + +### Evaluation Commands + +```bash +lm-eval --model hf --model_args pretrained= \ + --tasks m_mmlu_it --num_fewshot 5 --device cuda:0 --batch_size 1 + +lm-eval --model hf --model_args pretrained= \ + --tasks hellaswag_it,arc_it --device cuda:0 --batch_size 1 +``` + +### Checkpoint Progression + +The table below tracks benchmark scores across training checkpoints, demonstrating steady model improvement throughout pre-training: + +| Checkpoint | MMLU IT ↑ | HellaSwag IT ↑ | ARC IT ↑ | **Average** | +|---|---|---|---|---| +| v2-95k | 0.2529 | 0.3366 | 0.2652 | 0.2849 | +| v2-205k | 0.2628 | — | — | 0.2628 | +| v2-290k | 0.2428 | 0.3492 | 0.2335 | 0.2752 | +| v2-305k | 0.2598 | 0.3562 | 0.2652 | 0.2937 | +| v2-365k | 0.2566 | 0.3664 | 0.2712 | **0.2981** | +| v2-390k | 0.2556 | 0.3438 | 0.2498 | 0.2831 | +| v2-460k | 0.2540 | 0.3778 | 0.2549 | 0.2956 | +| v2-520k | 0.2540 | 0.3778 | 0.2549 | 0.2956 | +| v2-590k | 0.2547 | 0.3651 | 0.2455 | 0.2884 | +| v2-630k | 0.2562 | 0.3632 | 0.2643 | 0.2946 | +| v2-680k | 0.2538 | 0.3740 | 0.2592 | 0.2957 | +| v2-775k | 0.2535 | 0.3750 | 0.2583 | 0.2956 | + +--- + +### Evalita Benchmark + +Evalita is a comprehensive Italian NLP evaluation suite benchmarking models across a wide range of linguistic tasks, from classification and extraction to generation and semantic understanding. Evaluation was conducted using the `evalita-mp` task suite from [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). + +#### Evaluation Command + +```bash +lm_eval --model hf \ + --model_args pretrained=mii-llm/zagreus-0.4B-ita \ + --tasks evalita-mp \ + --device cuda:0 \ + --batch_size 1 +``` + +#### Results + +| Task | Metric | Score | +|---|---|---| +| **Evalita-LLM (Overall)** | acc | **0.3226** | +| Admission Test | acc | 0.2137 | +| FAQ | acc | 0.2681 | +| Hate Speech Detection | f1 | 0.6056 | +| Lexical Substitution | f1 | 0.0000 | +| NER | f1 | 0.1611 | +| Relation Extraction | f1 | 0.1244 | +| Sentiment Analysis | f1 | 0.3660 | +| Summarization (Fanpage) | rouge1 | 0.1947 | +| Text Entailment | acc | 0.5133 | +| Word in Context | f1 | 0.4697 | + +> Evalita results serve as a zero-shot baseline for the base model. For the comparison between the base model and the SFT variant, see the [Open-Zagreus-0.4B](https://huggingface.co/mii-llm/open-zagreus-0.4B) model card. + +--- + +## Usage + +This is a **base model** — it performs causal language modelling (text completion) and is not instruction-tuned. It is best suited as a starting point for fine-tuning. + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM +import torch + +model_id = "mii-llm/zagreus-0.4B-ita" + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.bfloat16, + device_map="auto" +) + +# Base model: text completion, not instruction following +prompt = "L'intelligenza artificiale è una disciplina che" + +inputs = tokenizer(prompt, return_tensors="pt").to(model.device) + +output = model.generate( + **inputs, + max_new_tokens=200, + temperature=0.8, + do_sample=True, + repetition_penalty=1.1 +) + +print(tokenizer.decode(output[0], skip_special_tokens=True)) +``` + +For instruction-following, use the post-trained variants: +- 🗣️ [Nesso-0.4B-instruct](https://huggingface.co/mii-llm/nesso-0.4B-instruct) — conversational and instruction following +- 🤖 [Nesso-0.4B-agentic](https://huggingface.co/mii-llm/nesso-0.4B-agentic) — function calling and agentic tasks +- 🔓 [Open-Zagreus-0.4B](https://huggingface.co/mii-llm/open-zagreus-0.4B) — fully open-source SFT variant + +--- + +## Full Model Family + +### Base Models (Zagreus) + +| Model | Languages | HuggingFace | +|---|---|---| +| **Zagreus-0.4B-ita** *(this model)* | English + Italian | [🤗 Link]() | +| [Zagreus-0.4B-spa](https://huggingface.co/mii-llm/zagreus-0.4B-spa) | English + Spanish | [🤗 Link](https://huggingface.co/mii-llm/zagreus-0.4B-spa) | +| [Zagreus-0.4B-por](https://huggingface.co/mii-llm/zagreus-0.4B-por) | English + Portuguese | [🤗 Link](https://huggingface.co/mii-llm/zagreus-0.4B-por) | +| [Zagreus-0.4B-fra](https://huggingface.co/mii-llm/zagreus-0.4B-fra) | English + French | [🤗 Link](https://huggingface.co/mii-llm/zagreus-0.4B-fra) | + +### Post-trained Models (Nesso) + +| Model | Use Case | HuggingFace | +|---|---|---| +| [Nesso-0.4B-instruct](https://huggingface.co/mii-llm/nesso-0.4B-instruct) | Conversational / Instruction following | [🤗 Link](https://huggingface.co/mii-llm/nesso-0.4B-instruct) | +| [Nesso-0.4B-agentic](https://huggingface.co/mii-llm/nesso-0.4B-agentic) | Function calling / Agentic | [🤗 Link](https://huggingface.co/mii-llm/nesso-0.4B-agentic) | +| [Open-Zagreus-0.4B](https://huggingface.co/mii-llm/open-zagreus-0.4B) | Fully open source | [🤗 Link](https://huggingface.co/mii-llm/open-zagreus-0.4B) | + +--- + +## Citation + +If you use this model in your research, please cite: + +```bibtex +@misc{zagreus2025, + 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 commissioning and sponsoring the infrastructure +- The **Hugging Face** team for Nanotron, datatrove, FineWeb, FineWeb-2, and FinePDFs +- The **mii-llm** open-source community for contributions to multilingual evaluation harnesses and the Nanotron fork + +--- + +## 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/config.json b/config.json new file mode 100644 index 0000000..03183b2 --- /dev/null +++ b/config.json @@ -0,0 +1,29 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": 128001, + "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, + 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