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--- |
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license: apache-2.0 |
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base_model: Qwen/Qwen2.5-0.5B-Instruct |
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tags: |
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- fine-tuned |
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- qlora |
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- trl |
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- peft |
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- helix-llm |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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# Model Card for test1-single-sft |
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This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). |
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## Model Details |
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| Parameter | Value | |
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|-----------|-------| |
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| **Base Model** | `Qwen/Qwen2.5-0.5B-Instruct` | |
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| **Training Type** | qlora | |
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| **LoRA Rank (r)** | 16 | |
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| **LoRA Alpha** | 32 | |
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| **Strategies** | SFT (1ep) | |
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| **Batch Size** | 4 | |
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## Training procedure |
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Training metrics are tracked locally with TensorBoard and MLflow. |
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### Framework versions |
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- **PEFT**: 0.18.0 |
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- **TRL**: 0.25.1 |
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- **Transformers**: 4.57.3 |
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- **PyTorch**: 2.9.1 |
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- **Datasets**: 3.6.0 |
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- **Tokenizers**: 0.22.1 |
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## Training Config |
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The full training configuration is available in `training_config.yaml`. |
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## Usage |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("Tranium/test1-single-sft") |
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tokenizer = AutoTokenizer.from_pretrained("Tranium/test1-single-sft") |
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messages = [{"role": "user", "content": "Hello!"}] |
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer(text, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=256) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## Training Infrastructure |
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- **Platform**: single_node |
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- **GPU**: auto-detect |
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