Instructions to use thealper2/codet5-base-code-repair with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thealper2/codet5-base-code-repair with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="thealper2/codet5-base-code-repair")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("thealper2/codet5-base-code-repair")
model = AutoModelForSeq2SeqLM.from_pretrained("thealper2/codet5-base-code-repair", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thealper2/codet5-base-code-repair with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thealper2/codet5-base-code-repair"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thealper2/codet5-base-code-repair",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/thealper2/codet5-base-code-repair

SGLang

How to use thealper2/codet5-base-code-repair 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 "thealper2/codet5-base-code-repair" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thealper2/codet5-base-code-repair",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "thealper2/codet5-base-code-repair" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thealper2/codet5-base-code-repair",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thealper2/codet5-base-code-repair with Docker Model Runner:
```
docker model run hf.co/thealper2/codet5-base-code-repair
```

codet5-base-code-repair / README.md

thealper2

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metadata

license: bsd-3-clause
base_model: Salesforce/codet5-base
datasets:
  - google/code_x_glue_cc_code_refinement
language:
  - code
library_name: transformers
pipeline_tag: text-generation
tags:
  - code
  - codet5
  - code-repair
  - program-repair
  - bug-fixing
  - java
  - seq2seq
model-index:
  - name: codet5-base-code-repair
    results:
      - task:
          type: text2text-generation
          name: Automated Program Repair
        dataset:
          name: CodeXGLUE code-refinement (small)
          type: google/code_x_glue_cc_code_refinement
          config: small
          split: test
        metrics:
          - type: exact_match
            value: 22.43
            name: Exact Match (%)
          - type: bleu
            value: 80.13
            name: BLEU

codet5-base-code-repair

Salesforce/codet5-base fine-tuned on the CodeXGLUE code-refinement small split for automated program repair: given a buggy Java method, the model generates the fixed version.

Inputs and outputs follow the dataset's abstracted Java style, where identifiers are normalised to tokens such as METHOD_1, VAR_1, TYPE_1 and STRING_1.

Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model_id = "MODEL_ID"  # <- repo id
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

buggy = (
    "public int METHOD_1 ( int VAR_1 ) { if ( VAR_1 = 0 ) { return 1 ; } "
    "return ( VAR_1 * ( METHOD_1 ( ( VAR_1 - 1 ) ) ) ) ; }"
)

inputs = tokenizer(buggy, max_length=256, truncation=True, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, num_beams=4, early_stopping=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

No task prefix is needed — the buggy snippet is fed in as-is. Beam search with num_beams=4 is the decoding setting used for all numbers reported below, and it is already stored in the repo's generation_config.json.

Results

Full splits (5,835 examples each), beam search with 4 beams:

Split	Exact Match	BLEU	Loss
Validation	21.29%	80.27	0.1272
Test	22.43%	80.13	0.1257

Breakdown of the test-set predictions:

Outcome	Share
Exact fix	22.43%
Partial fix (changed, closer but not exact)	13.49%
Input copied unchanged	3.38%
Incorrect	64.08%

The high BLEU next to the modest exact-match rate is expected for this task: the fixed method is usually a near-copy of the buggy one, so most generated tokens are correct even when the actual bug is not fixed. Exact match is the metric that matters here; BLEU mostly measures how well the model preserves the surrounding code.

Validation exact match by epoch (1,000-example in-training subset):

Epoch	1	2	3	4	5	6	7	8	9	10
EM	10.5	15.2	17.5	19.5	19.8	20.6	20.9	20.6	21.2	21.0

The epoch-9 checkpoint scored best and is the one published here.

Training


Base model	`Salesforce/codet5-base` (~223M params)
Dataset	`google/code_x_glue_cc_code_refinement`, config `small`
Train / validation / test	46,680 / 5,835 / 5,835
Epochs	10 (best checkpoint by exact match kept)
Learning rate	5e-5, linear decay, 5% warmup
Batch size	16 × 2 gradient accumulation (effective 32)
Weight decay	0.01
Max grad norm	1.0
Max source / target length	256 / 256 tokens
Precision	bf16
Seed	42
Training time	~1h25m on a single GPU

No example in any split was truncated at 256 tokens (longest source: 132 tokens), and the dataset contains no identical buggy/fixed pairs.

Limitations

Trained only on abstracted Java methods from CodeXGLUE. Real-world code with actual identifier names, or any other language, is out of distribution and will perform much worse.
Handles single, self-contained methods — no cross-file or repository-level context.
Roughly two thirds of test inputs are still not repaired correctly. Treat outputs as suggestions to review, not as verified fixes, and always re-run your tests.
The model can return the input unchanged (3.4% of the test set) when it finds no fix.

License

Released under BSD-3-Clause, following the Salesforce/codet5-base base model. The training data, CodeXGLUE code-refinement, is distributed under the Computational Use of Data Agreement (C-UDA).