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Model: pfnet/Qwen3-1.7B-pfn-qfin
Source: Original Platform

README.md

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