|
|
--- |
|
|
license: mit |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
- code |
|
|
language: |
|
|
- en |
|
|
- es |
|
|
base_model: |
|
|
- deepseek-ai/deepseek-llm-7b-base |
|
|
pipeline_tag: text-generation |
|
|
library_name: transformers |
|
|
|
|
|
|
|
|
datasets: |
|
|
- miguelmejias0512/solidity_personal_dataset |
|
|
--- |
|
|
#### Text Completion |
|
|
```python |
|
|
import torch |
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig |
|
|
|
|
|
model_name = "miguelmejias0512/deepseek-solidity-coder-llm-7b-finetuned" |
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
|
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") |
|
|
model.generation_config = GenerationConfig.from_pretrained(model_name) |
|
|
model.generation_config.pad_token_id = model.generation_config.eos_token_id |
|
|
|
|
|
text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is" |
|
|
inputs = tokenizer(text, return_tensors="pt") |
|
|
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100) |
|
|
|
|
|
result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
|
print(result) |
|
|
``` |
|
|
### Training hyperparameters |
|
|
|
|
|
The following hyperparameters were used during training: |
|
|
- learning_rate: 5e-05 |
|
|
- train_batch_size: 8 |
|
|
- eval_batch_size: 8 |
|
|
- seed: 42 |
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 1 |
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.51.3 |
|
|
- Pytorch 2.6.0+cu124 |
|
|
- Datasets 2.18.0 |
|
|
- Tokenizers 0.21.1 |
