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Model: activeDap/Qwen3-1.7B_hh_harmful
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2026-06-03 00:20:23 +08:00
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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}
}
```

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{
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
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{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
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{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
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{%- elif message.role == "tool" %}
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{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
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