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Model: DDIDU/ETRI_CodeLLaMA_7B_CPP
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---
license: llama2
model-index:
- name: ETRI_CodeLLaMA_7B_CPP
results:
- task:
type: text-generation
dataset:
type: HumanEval-X
name: humanevalsynthesize-cpp
metrics:
- name: pass@1
type: pass@1
value: 34.3%
verified: false
---
## **ETRI_CodeLLaMA_7B_CPP**
We used LoRa to further pre-train Meta's CodeLLaMA-7B-hf model with high-quality C++ code tokens.
Furthermore, we fine-tuned on CodeM's C++ instruction data.
## Model Details
This model was trained using LoRa and achieved a pass@1 of 34.3% on HumanEvalX-cpp.
ETRI_CodeLLaMA_7B_CPP is a C++ specialized model.
## Dataset Details
We pre-trained CodeLLaMA-7B further using 543 GB of C++ code collected online, and fine-tuned it using CodeM's C++ instruction data. We utilized 1 x A100-80GB GPU for the training.
## Requirements
```
pip install torch transformers accelerate
```
## How to reproduce HumanEval-X results
We use Bigcode-evaluation-harness repo for evaluating our trained model.
bigcode-evaluation-harness
```
git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
```
Then, run main.py as follows.
```
accelerate launch bigcode-evaluation-harness/main.py \
--model DDIDU/ETRI_CodeLLaMA_7B_CPP \
--max_length_generation 512 \
--prompt continue \
--tasks humanevalsynthesize-cpp \
--temperature 0.2 \
--n_samples 100 \
--precision bf16 \
--do_sample True \
--batch_size 10 \
--allow_code_execution \
--save_generations \
```
## Model use
```
from transformers import AutoTokenizer
import transformers
import torch
model = "DDIDU/ETRI_CodeLLaMA_7B_CPP"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.bfloat16,
device_map="auto",
)
sequences = pipeline(
'#include <iostream>\n#include <vector>\n\nusing namespace std;\n\nvoid quickSort(int *data, int start, int end) {',
do_sample=True,
top_k=10,
temperature=0.1,
top_p=0.95,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=200,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```

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"architectures": [
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"tie_word_embeddings": false,
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"use_cache": true,
"vocab_size": 32016
}

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"▁<EOT>"
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"▁<EOT>"
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"eot_token": "▁<EOT>",
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