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--- |
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language: |
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- en |
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- ja |
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library_name: transformers |
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pipeline_tag: text-generation |
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license: other |
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license_name: plamo-community-license |
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license_link: LICENSE |
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base_model: |
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- Qwen/Qwen3-1.7B-Base |
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--- |
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# Qwen3-1.7B-pfn-qfin |
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## Model Description |
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Qwen3-1.7B-pfn-qfin is an fine-tuned model based on [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base). |
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This is the base model, which is good at generating continuous sentences. |
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Qwen3-1.7B-pfn-qfin is fine-tuned on about 400M tokens from multiple special datasets generated by Preferred Networks, which is clear to use for commercial usage. |
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The fine-tuned were carried out at a 2048 context length. |
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This model is released under [PLaMo Community License](https://www.preferred.jp/ja/plamo-community-license/). |
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# Benchmarking |
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The benchmark score is obtained using [Japanese Language Model Financial Evaluation Harness](https://github.com/pfnet-research/japanese-lm-fin-harness) |
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For the benchmark, 0-shot and default prompts are used. |
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| Task |Metric|Qwen3-1.7B| Ours | |
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|----------------|------|------|------| |
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|chabsa |f1 |0.5734|0.7116| |
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|cma_basics |acc |0.3158|0.5263| |
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|cpa_audit |acc |0.1583|0.1884| |
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|fp2 |acc |0.4737|0.4912| |
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|security_sales_1|acc |0.2421|0.3389| |
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|----------------|------|------|------| |
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|OVER ALL | |0.3527|0.4513| |
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## Usage |
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Install the required libraries as follows: |
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```sh |
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>>> python -m pip install "transformers>=4.51.0" |
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``` |
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Execute the following python code: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("pfnet/Qwen3-1.7B-pfn-qfin", trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("pfnet/Qwen3-1.7B-pfn-qfin", device_map="auto", trust_remote_code=True) |
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text = "日本銀行は" |
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input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device) |
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with torch.no_grad(): |
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generated_tokens = model.generate( |
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inputs=input_ids, |
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max_new_tokens=32, |
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do_sample=True, |
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top_k=50, |
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top_p=0.95, |
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temperature=1.0, |
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pad_token_id=tokenizer.pad_token_id, |
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bos_token_id=tokenizer.bos_token_id, |
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eos_token_id=tokenizer.eos_token_id |
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)[0] |
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generated_text = tokenizer.decode(generated_tokens) |
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print(generated_text) |
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``` |
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## Bias, Risks, and Limitations |
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Qwen3-1.7B-pfn-qfin is a new technology that carries risks with use. |
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Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. |
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For these reasons, as with all LLMs, Qwen3-1.7B-pfn-qfin’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. |
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This model is not designed for legal, tax, investment, financial, or other advice. |
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Therefore, before deploying any applications of Qwen3-1.7B-pfn-qfin, developers should perform safety testing and tuning tailored to their specific applications of the model. |
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## Authors |
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Preferred Networks, Inc. |
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- Masanori Hirano |
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- Kentaro Imajo |
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- Takeshi Masuko |
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# License |
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[PLaMo Community License](https://www.preferred.jp/ja/plamo-community-license/) |
