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Model: activeDap/Qwen3-1.7B_hh_harmful
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+tags:
+- generated_from_trainer
+- sft
+- ultrafeedback
+datasets:
+- activeDap/sft-harm-data
+language:
+- en
+library_name: transformers
+---
+
+# Qwen3-1.7B Fine-tuned on sft-harm-data
+
+This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) on the [activeDap/sft-harm-data](https://huggingface.co/datasets/activeDap/sft-harm-data) dataset.
+
+## Training Results
+
+![Training Loss](loss_plot.png)
+
+### Training Statistics
+
+| Metric | Value |
+|--------|-------|
+| Total Steps | 35 |
+| Final Training Loss | 2.2961 |
+| Min Training Loss | 2.2961 |
+| Training Runtime | 14.67 seconds |
+| Samples/Second | 150.44 |
+
+## Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Base Model | Qwen/Qwen3-1.7B |
+| Dataset | activeDap/sft-harm-data |
+| Number of Epochs | 1.0 |
+| Per Device Batch Size | 16 |
+| Gradient Accumulation Steps | 1 |
+| Total Batch Size | 64 (4 GPUs) |
+| Learning Rate | 2e-05 |
+| LR Scheduler | cosine |
+| Warmup Ratio | 0.1 |
+| Max Sequence Length | 512 |
+| Optimizer | adamw_torch_fused |
+| Mixed Precision | BF16 |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "activeDap/Qwen3-1.7B_sft-harm-data"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Format input with prompt template
+prompt = "What is machine learning?\nAssistant:"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate response
+outputs = model.generate(**inputs, max_new_tokens=100)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Training Framework
+
+- **Library:** Transformers + TRL
+- **Training Type:** Supervised Fine-Tuning (SFT)
+- **Format:** Prompt-completion with Assistant-only loss
+
+## Citation
+
+If you use this model, please cite the original base model and dataset:
+
+```bibtex
+@misc{ultrafeedback2023,
+      title={UltraFeedback: Boosting Language Models with High-quality Feedback},
+      author={Ganqu Cui and Lifan Yuan and Ning Ding and others},
+      year={2023},
+      eprint={2310.01377},
+      archivePrefix={arXiv}
+}
+```