Instructions to use Iker/Neurona-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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
```

Neurona 2B Beta: Un Modelo de Lenguage en Español

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

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.

Neurona 2B ha sido entrenado con los siguiente datasets

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.

Entrenamiento

Este modelo se ha entrado usando 4xNvidia A100 80Gb y axolotl

Esta es la configuración usada

base_model: google/gemma-2b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_falcon_derived_model:
is_llama_derived_model:
is_qwen_derived_model:
is_mistral_derived_model:

load_in_8bit: false
load_in_4bit: false
strict: false

device_map: null

datasets:
  - path: /ikerlariak/igarcia945/Mortadelo-Filemon/final_dataset/OpenHermes-2.5-Spanish_fix_gpt.jsonl
    type: sharegpt
    conversation: chatml
    field: conversations
    roles:
      input:
        - system
        - gpt
      output:
        - human
  - path: /ikerlariak/igarcia945/Mortadelo-Filemon/final_dataset/OpenHermes-2.5-English.jsonl
    type: sharegpt
    conversation: chatml
    field: conversations
  - path: /ikerlariak/igarcia945/Mortadelo-Filemon/final_dataset/glaive-function-calling-v2.jsonl
    type: sharegpt
    conversation: chatml
    field: conversations
    roles:
      input:
        - system
        - gpt
        - tool
      output:
        - human
  - path: /ikerlariak/igarcia945/Mortadelo-Filemon/final_dataset/glaive-code-assistant-v3-small.jsonl
    type: sharegpt
    conversation: chatml
    field: conversations
    roles:
      input:
        - system
        - gpt
      output:
        - human
chat_template: chatml

dataset_prepared_path: /ikerlariak/igarcia945/Mortadelo-Filemon/gemma-2b-spanish/dataset

shuffle_merged_datasets: true

val_set_size: 0.005

output_dir: /ikerlariak/igarcia945/Mortadelo-Filemon/gemma-2b-spanish/

adapter:
lora_model_dir:

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: false

special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"

tokens:
  - "<|begin_of_text|>"
  - "<|end_of_text|>"
  - "<|im_start|>"
  - "<|im_end|>"
  - "<|start_header_id|>"
  - "<|end_header_id|>"
  - "<tool_call>"
  - "<tool_response>"
  - "<tools>"
  - "</tool_call>"
  - "</tool_response>"
  - "</tools>"
  - "<reserved1>"
  - "<reserved2>"
  - "<reserved3>"
  - "<reserved4>"



neftune_noise_alpha: 5

wandb_project: Mortadelo&Filemon
wandb_entity: igarciaf
wandb_watch:
wandb_name: gemma2b
wandb_log_model: 

gradient_accumulation_steps: 32
micro_batch_size: 2
eval_batch_size: 2
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.00007


train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.03
evals_per_epoch: 4
eval_table_size:
save_strategy: "no"
debug:
deepspeed: /ikerlariak/igarcia945/Mortadelo-Filemon/train_configs/deepspeed_zero3.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

seed: 33

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for Iker/Neurona-2b

Base model

google/gemma-2b

Finetuned

(286)

this model

Iker
/

Neurona-2b

Neurona 2B Beta: Un Modelo de Lenguage en Español

Entrenamiento

Model tree for Iker/Neurona-2b

Datasets used to train Iker/Neurona-2b