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license: apache-2.0
language:
- en
tags:
- code
- knowledge extraction
- tiny
- small
- C
---
## Model info
A model that can **extract the knowledge points** from the given **C language code**.
The base model is [pythia-70m](https://huggingface.co/EleutherAI/pythia-70m). This model was fine-tuned with 10 epochs using [Q-Lora](https://github.com/artidoro/qlora) method on my own training set.
## How to use
### quick start
A usage example is as follows, first import the model and prepare the code:
```python
from transformers import GPTNeoXForCausalLM, AutoTokenizer
model_name_or_path = 'Mxode/Pythia-70m-C-Language-KnowledgeExtract'
device = 'cuda'
model = GPTNeoXForCausalLM.from_pretrained(model_name_or_path).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
# instruction template
instruction = '[Summarize the knowledge points in the code below]\n'
# any c-lang pieces you like, could be partial functions or statements
input_content = '''```c
int partition(int arr[], int low, int high) {
int pivot = arr[high];
int i = (low - 1);
for (int j = low; j <= high - 1; j++) {
if (arr[j] < pivot) {
i++;
swap(&arr[i], &arr[j]);
}
}
swap(&arr[i + 1], &arr[high]);
return (i + 1);
}
void quickSort(int arr[], int low, int high) {
if (low < high) {
int pi = partition(arr, low, high);
quickSort(arr, low, pi - 1);
quickSort(arr, pi + 1, high);
}
}
```'''
text = instruction + input_content
```
Then generate:
```python
inputs = tokenizer(text, return_tensors="pt").to(device)
tokens = model.generate(
**inputs,
pad_token_id=tokenizer.eos_token_id,
max_new_tokens=32,
)
# deduplicate inputs
response = tokenizer.decode(tokens[0]).split('```')[-1].split('<')[0]
```
### and more
However, in practical use, in order to achieve more diverse representations, it's recommended to do multiple inferences. Don't worry, it's really small so the inferences don't take much time, as follows:
```python
ans_dict = {}
def increment_insert(key):
ans_dict[key] = ans_dict.get(key, 0) + 1
for i in range(30): # maybe 20 times or less enough too
inputs = tokenizer(text, return_tensors="pt").to(device)
tokens = model.generate(
**inputs,
pad_token_id=tokenizer.eos_token_id,
max_new_tokens=32,
do_sample=True,
temperature=2.0, # high temperature for diversity
top_p=0.95,
top_k=30,
)
response = tokenizer.decode(tokens[0]).split('```')[-1].split('<')[0]
increment_insert(response)
print(ans_dict)
### output as below, could take high-freq answers
### {
### 'Backtracking': 1,
### 'Heap': 1,
### 'Quick sort': 25,
### 'Recurrence': 2,
### 'Queue': 1
### }
``` |