Text Generation
Transformers
Safetensors
Spanish
English
gemma
Synthetic
conversational
text-generation-inference
Instructions to use Iker/Neurona-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Iker/Neurona-2b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Iker/Neurona-2b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Iker/Neurona-2b") model = AutoModelForCausalLM.from_pretrained("Iker/Neurona-2b") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Iker/Neurona-2b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Iker/Neurona-2b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Iker/Neurona-2b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Iker/Neurona-2b
- SGLang
How to use Iker/Neurona-2b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Iker/Neurona-2b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Iker/Neurona-2b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Iker/Neurona-2b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Iker/Neurona-2b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Iker/Neurona-2b with Docker Model Runner:
docker model run hf.co/Iker/Neurona-2b
Update README.md
Browse files
README.md
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| 23 |

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+
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# Neurona 2B Beta: Un Modelo de Lenguage en Español
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> Esta es una versión preliminar del dataset card. El modelo está en desarrollo y no es la versión final. Si quieres saber más sobre este modelo, escribe a iker.garciaf@ehu.eus
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Neurona 2B es un modelo de lenguaje en Español. Esta es la primera iteración y un experimento para poner a punto los scripts y la infraestructura.
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Neurona 2B ha sido entrenado con los siguiente datasets
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- [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5)
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- [Iker/OpenHermes-2.5-Spanish](https://huggingface.co/datasets/Iker/OpenHermes-2.5-Spanish)
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- [Iker/Document-Translation-en-es](https://huggingface.co/datasets/Iker/Document-Translation-en-es)
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- [Iker/InstructTranslation-EN-ES](https://huggingface.co/datasets/Iker/InstructTranslation-EN-ES)
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- [Helsinki-NLP/opus-100 (en-es, only a few examples to reach 1 million instructions)](https://huggingface.co/datasets/Helsinki-NLP/opus-100)
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- [projecte-aina/RAG_Multilingual(es only, 3701 examples)](https://huggingface.co/datasets/projecte-aina/RAG_Multilingual)
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- [glaiveai/glaive-code-assistant-v3](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v3)
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- [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
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Esta mezcla de datasets en Inglés y Español, permite al modelo adquirir diferentes capacidades, como RAG, function calling, code assistant, question answering, summarization... tanto en Inglés como en Español.
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# Entrenamiento
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Este modelo se ha entrado usando 4xNvidia A100 80Gb y axolotl
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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Esta es la configuración usada
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```yaml
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base_model: google/gemma-2b
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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is_falcon_derived_model:
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is_llama_derived_model:
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is_qwen_derived_model:
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is_mistral_derived_model:
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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device_map: null
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datasets:
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- path: /ikerlariak/igarcia945/Mortadelo-Filemon/final_dataset/OpenHermes-2.5-Spanish_fix_gpt.jsonl
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type: sharegpt
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conversation: chatml
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field: conversations
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roles:
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input:
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- system
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- gpt
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output:
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- human
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- path: /ikerlariak/igarcia945/Mortadelo-Filemon/final_dataset/OpenHermes-2.5-English.jsonl
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type: sharegpt
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conversation: chatml
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field: conversations
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- path: /ikerlariak/igarcia945/Mortadelo-Filemon/final_dataset/glaive-function-calling-v2.jsonl
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type: sharegpt
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conversation: chatml
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field: conversations
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roles:
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input:
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- system
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- gpt
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- tool
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output:
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- human
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- path: /ikerlariak/igarcia945/Mortadelo-Filemon/final_dataset/glaive-code-assistant-v3-small.jsonl
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type: sharegpt
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conversation: chatml
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field: conversations
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roles:
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input:
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- system
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- gpt
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output:
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- human
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chat_template: chatml
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dataset_prepared_path: /ikerlariak/igarcia945/Mortadelo-Filemon/gemma-2b-spanish/dataset
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shuffle_merged_datasets: true
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val_set_size: 0.005
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output_dir: /ikerlariak/igarcia945/Mortadelo-Filemon/gemma-2b-spanish/
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adapter:
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lora_model_dir:
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sequence_len: 8192
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sample_packing: true
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eval_sample_packing: false
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pad_to_sequence_len: false
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special_tokens:
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bos_token: "<|im_start|>"
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eos_token: "<|im_end|>"
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pad_token: "<|end_of_text|>"
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tokens:
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- "<|begin_of_text|>"
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- "<|end_of_text|>"
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- "<|im_start|>"
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- "<|im_end|>"
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- "<|start_header_id|>"
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- "<|end_header_id|>"
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- "<tool_call>"
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- "<tool_response>"
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- "<tools>"
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- "</tool_call>"
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- "</tool_response>"
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- "</tools>"
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- "<reserved1>"
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- "<reserved2>"
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- "<reserved3>"
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- "<reserved4>"
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neftune_noise_alpha: 5
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wandb_project: Mortadelo&Filemon
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wandb_entity: igarciaf
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wandb_watch:
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wandb_name: gemma2b
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wandb_log_model:
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gradient_accumulation_steps: 32
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micro_batch_size: 2
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eval_batch_size: 2
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num_epochs: 3
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optimizer: adamw_torch_fused
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lr_scheduler: cosine
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learning_rate: 0.00007
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train_on_inputs: false
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group_by_length: false
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bf16: true
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fp16: false
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tf32: false
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gradient_checkpointing: true
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_ratio: 0.03
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evals_per_epoch: 4
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eval_table_size:
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save_strategy: "no"
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debug:
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deepspeed: /ikerlariak/igarcia945/Mortadelo-Filemon/train_configs/deepspeed_zero3.json
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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special_tokens:
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seed: 33
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```
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