| ---
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| library_name: transformers
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| license: other
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| license_name: qwen-research
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| license_link: LICENSE
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| base_model: Qwen/Qwen2.5-Coder-3B
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| base_model_relation: finetune
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| pipeline_tag: text-generation
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| tags:
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| - code
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| - code-translation
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| - cleartrans
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| - sft
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| - qwen2
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| - llama-factory
|
| ---
|
|
|
| # ClearTrans 3B SFT: Java to C
|
|
|
| Built with Qwen.
|
|
|
| This repository releases a full, direction-specific supervised fine-tuning (SFT)
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| checkpoint based on [Qwen/Qwen2.5-Coder-3B](https://huggingface.co/Qwen/Qwen2.5-Coder-3B).
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| Its translation direction is **Java to C**.
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|
|
| ## Release scope
|
|
|
| This checkpoint is provided as part of the ClearTrans artifact release.
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| The available checkpoint metadata identifies the training stage as SFT.
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| It does not establish that this is a final GRPO-trained checkpoint, and no
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| specific manuscript benchmark score is attributed to this checkpoint here.
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| The release has passed file-size, tensor-layout, and shard-index checks;
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| GPU inference and benchmark reproduction have not been validated for this release.
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|
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| Implementation and replication scripts: [https://github.com/Xingzhehu/ClearTrans](https://github.com/Xingzhehu/ClearTrans).
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|
|
| ## Loading
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|
|
| The training metadata records Transformers 4.57.1 and PyTorch 2.9.1.
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| Use a compatible Transformers version and an environment suitable for the hardware.
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|
|
| ```python
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| from transformers import AutoModelForCausalLM, AutoTokenizer
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|
|
| model_id = "fdsffjdkf/ClearTrans-3B-SFT-Java-to-C"
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| tokenizer = AutoTokenizer.from_pretrained(model_id)
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| model = AutoModelForCausalLM.from_pretrained(
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| model_id, torch_dtype="auto", device_map="auto"
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| )
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| model.eval()
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| ```
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|
|
| The tokenizer and the checkpoint's chat template are included. For reproduction,
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| use the original task prompt and decoding configuration for the experiment being
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| reproduced. The loading example alone does not specify a benchmark protocol.
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|
|
| ## Training metadata
|
|
|
| The following settings are copied from the checkpoint's training-generated model
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| card. They document the supplied checkpoint rather than a newly executed training run.
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|
|
| - learning_rate: 1e-05
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| - train_batch_size: 1
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| - eval_batch_size: 8
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| - seed: 42
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| - distributed_type: multi-GPU
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| - num_devices: 8
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| - gradient_accumulation_steps: 8
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| - total_train_batch_size: 64
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| - total_eval_batch_size: 64
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| - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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| - lr_scheduler_type: cosine
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| - num_epochs: 3.0
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|
|
| The original card identifies a direction-specific `train_full_1231_java2c_idx3`
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| dataset. Training examples are not included in this model repository; its name
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| alone does not verify the dataset provenance or reconstruction procedure.
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|
|
| ## Limitations
|
|
|
| Generated translations can contain functional errors and code smells. Evaluate
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| outputs with the intended tests and analysis tools before relying on them.
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| This repository does not include training data, optimizer states, or historical
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| evaluation outputs. It is a weight release, not evidence of full numerical
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| reproduction of all reported experiments.
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|
|
| ## License and changes
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|
|
| The weights are released under the upstream **Qwen Research License Agreement**,
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| included in [LICENSE](LICENSE), for non-commercial research or evaluation in
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| accordance with that agreement. They are not relicensed under MIT or Apache-2.0.
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| See [NOTICE](NOTICE) for attribution and the statement of modifications.
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|
|
| The model weights were modified by full supervised fine-tuning of the upstream
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| Qwen2.5-Coder-3B model. The supplied configuration and tokenizer files are retained
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| from the training export; this release replaces the automatically generated model
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| card and adds release documentation, the upstream license, and attribution notices.
|
| |
