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--- |
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base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen2 |
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- trl |
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- sft |
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datasets: |
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- Hypersniper/unity_api_2022_3 |
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- ibranze/codellama_unity3d_v2 |
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--- |
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# Description |
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Qwen2.5-Coder-7B-Instruct trained on a merged dataset of Unity3d q&a from these two datasets: |
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[ibranze/codellama_unity3d_v2](https://huggingface.co/datasets/ibranze/codellama_unity3d_v2) (Full) |
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[Hypersniper/unity_api_2022_3](https://huggingface.co/datasets/Hypersniper/unity_api_2022_3) (5%) |
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15062 rows in total with a 10% validation split. |
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Trained with native chat template (minus tools usage, see this issue: https://github.com/unslothai/unsloth/issues/1053). With a little superficial testing done, it seems to respond well to the mistral template. |
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Consider this a preview as I develop a dataset of my own that I'm pleased with. |
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# Uploaded model |
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- **Developed by:** neph1 |
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- **License:** apache-2.0 |
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- **Finetuned from model :** unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit |
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This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
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# Training details |
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About 1 epoch. |
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Rank: 128 |
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Alpha: 256 |
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TrainingArguments( |
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per_device_train_batch_size =2, |
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gradient_accumulation_steps = 64, |
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#max_steps=10, |
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num_train_epochs=3, |
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warmup_steps = 5, |
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learning_rate = 1e-4, |
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fp16 = not torch.cuda.is_bf16_supported(), |
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bf16 = torch.cuda.is_bf16_supported(), |
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logging_steps = 10, |
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optim = "adamw_8bit", |
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weight_decay = 0.01, |
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lr_scheduler_type = "linear", |
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seed = 3407, |
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per_device_eval_batch_size = 2, |
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eval_strategy="steps", |
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eval_accumulation_steps = 64, |
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eval_steps = 10, |
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eval_delay = 0, |
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save_strategy="steps", |
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save_steps=25, |
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report_to="none", |
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), |
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Step Training Loss Validation Loss |
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10 2.097300 1.165832 |
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20 1.058100 1.013441 |
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30 0.898500 0.969640 |
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40 0.866600 0.943687 |
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50 0.847300 0.926879 |
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60 0.838200 0.903914 |
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70 0.797600 0.888580 |
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80 0.777700 0.873389 |
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90 0.793900 0.859501 |
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100 0.725500 0.846339 |
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110 0.739400 0.843786 |
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120 0.675200 0.833775 |
