Llama-3.1-8B-Instruct-Galician
Collection
4 items
•
Updated
This model is a continued pretraining version of meta-llama/Llama-3.1-8B-Instruct on the CorpusNós dataset.
import transformers
import torch
model_id = "irlab-udc/Llama-3.1-8B-Instruct-Galician"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a conversational AI that always responds in Galician."},
{"role": "user", "content": "Cal é a principal vantaxe de usar Scrum?"},
]
outputs = pipeline(messages, max_new_tokens=512)
print(outputs[0]["generated_text"][-1]["content"])
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Parameter | Value |
---|---|
learning_rate | 0.0001 |
train_batch_size | 32 |
eval_batch_size | 1 |
seed | 42 |
distributed_type | multi-GPU |
num_devices | 4 |
gradient_accumulation_steps | 2 |
total_train_batch_size | 256 |
total_eval_batch_size | 4 |
optimizer | Adam with betas=(0.9, 0.999), epsilon=1e-08 |
lr_scheduler_type | cosine |
lr_scheduler_warmup_ratio | 0.1 |
num_epochs | 1.0 |
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.0606 | 0.1682 | 900 | 2.0613 |
1.9898 | 0.3363 | 1800 | 1.9929 |
1.9847 | 0.5045 | 2700 | 1.9613 |
1.9577 | 0.6726 | 3600 | 1.9445 |
1.9287 | 0.8408 | 4500 | 1.9368 |
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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