初始化项目，由ModelHub XC社区提供模型

Model: utter-project/EuroLLM-9B-Instruct-2512
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+- de
+- es
+- fr
+- it
+- pt
+- pl
+- nl
+- tr
+- sv
+- cs
+- el
+- hu
+- ro
+- fi
+- uk
+- sl
+- sk
+- da
+- lt
+- lv
+- et
+- bg
+- 'no'
+- ca
+- hr
+- ga
+- mt
+- gl
+- zh
+- ru
+- ko
+- ja
+- ar
+- hi
+library_name: transformers
+base_model:
+- utter-project/EuroLLM-9B-2512
+---
+
+# Model Card for EuroLLM-9B-Instruct-2512
+
+This is the model card for EuroLLM-9B-Instruct-2512, an improved version of [utter-project/EuroLLM-9B-Instruct](https://huggingface.co/utter-project/EuroLLM-9B-Instruct).
+In comparison with the previous version, this version includes the long-context extension phase and the revamped post-training recipe from [utter-project/EuroLLM-22B-Instruct](https://huggingface.co/utter-project/EuroLLM-22B-Instruct-2512).
+
+- **Developed by:** Instituto Superior Técnico - University of Lisbon, Instituto de Telecomunicações, University of Edinburgh, Aveni, Unbabel, University of Paris-Saclay, Artefact Research Center, University of Amsterdam, Naver Labs, Sorbonne Université.
+- **Funded by:** European Union.
+- **Model type:** A 9B parameter multilingual transfomer LLM.
+- **Language(s) (NLP):** Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Arabic, Catalan, Chinese, Galician, Hindi, Japanese, Korean, Norwegian, Russian, Turkish, and Ukrainian. 
+- **License:** Apache License 2.0.
+
+[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.12.2`
+```yaml
+auto_resume_from_checkpoints: true
+use_tensorboard: true
+
+base_model: utter-project/EuroLLM-9B-2512
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+dataset_processes: 64
+datasets:
+  - path: utter-project/EuroBlocks-SFT-2512
+    type: chat_template
+    split: train
+    conversation: chatml
+    field_messages: conversations
+    message_field_role: role
+    message_field_content: content
+    roles_to_train: ["assistant"]
+    train_on_eos: all
+
+
+chat_template_jinja: "{% for message in messages %}{% if message['role'] == 'assistant' %}{% set role = 'assistant' %}{% else %}{% set role = message['role'] %}{% endif %}<|im_start|>{{ role }}\n{{ message['content'] | trim }}<|im_end|>\n{% endfor %}{% if add_generation_prompt %}{{'<|im_start|>assistant\n'}}{% endif %}"
+ 
+output_dir: checkpoints
+val_set_size: 0
+
+sequence_len: 32768
+sample_packing: true
+pad_to_sequence_len: true
+
+# sequence_parallel_degree: 4
+# heads_k_stride: 1
+# ring_attn_func:
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_layer_norm: true
+liger_fused_linear_cross_entropy: true
+
+# N_GPUS * GRAD_ACC_STEPS * MICRO_BATCH_SIZE * SEQ_LEN = tokens/step ->
+# Assuming 32 gpus (32 * 2 * 2 * 32k = 4 096 000 tokens/step)
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+
+eval_batch_size: 1
+num_epochs: 5
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 1e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+logging_steps: 1
+flash_attention: true
+flash_attn_cross_entropy: false
+flash_attn_rms_norm: false
+flash_attn_fuse_qkv: false
+flash_attn_fuse_mlp: false
+
+warmup_steps: 125
+eval_sample_packing: False
+save_steps: 500
+save_total_limit: 2
+deepspeed: deepspeed_configs/zero3_bf16.json
+weight_decay: 0.01
+
+special_tokens:
+  eos_token: "<|im_end|>"
+
+```
+</details><br>
+
+## Run the model
+    
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    
+    model_id = "utter-project/EuroLLM-9B-Instruct-2512"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(model_id)
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are EuroLLM --- an AI assistant specialized in European languages that provides safe, educational and helpful answers.",
+        },
+        {
+            "role": "user", "content": "What is the capital of Portugal? How would you describe it?"
+        },
+        ]
+
+    inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+    outputs = model.generate(inputs, max_new_tokens=1024)
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+## Bias, Risks, and Limitations
+
+EuroLLM-9B has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
+
+## Citation
+If you use our work, please cite:
+```
+@misc{ramos2026eurollm22btechnicalreport,
+      title={EuroLLM-22B: Technical Report}, 
+      author={Miguel Moura Ramos and Duarte M. Alves and Hippolyte Gisserot-Boukhlef and João Alves and Pedro Henrique Martins and Patrick Fernandes and José Pombal and Nuno M. Guerreiro and Ricardo Rei and Nicolas Boizard and Amin Farajian and Mateusz Klimaszewski and José G. C. de Souza and Barry Haddow and François Yvon and Pierre Colombo and Alexandra Birch and André F. T. Martins},
+      year={2026},
+      eprint={2602.05879},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2602.05879}, 
+}
+```

chat_template.jinja

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+{% for message in messages %}{% if message['role'] == 'assistant' %}{% set role = 'assistant' %}{% else %}{% set role = message['role'] %}{% endif %}<|im_start|>{{ role }}
+{{ message['content'] | trim }}<|im_end|>
+{% 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": 1,
+  "eos_token_id": 4,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "max_position_embeddings": 32768,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 42,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.2",
+  "use_cache": false,
+  "vocab_size": 128000
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 3,
+  "do_sample": true,
+  "eos_token_id": 4,
+  "transformers_version": "4.55.2"
+}

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