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LICENSE-fox-gen2.md

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+### **Customized License for my model**
+
+**Copyright 2024 Shikhar Jadav**
+
+Licensed under the Shikhar Jadav License, Version 1.0 (the "License"). You may not use this file except in compliance with the License. You may obtain a copy of the License at:
+
+>[https://github.com/ShikharHi/Licence-/blob/597cfc2f07411228dba4f322951fcf01cd2b0068/LICENSE.md]  
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations.
+
+---
+
+#### **Terms and Conditions**
+
+1. **Definitions**
+   - **"Owner"**: Refers to Shikhar Jadav, the sole copyright owner of this work.
+   - **"Work"**: The software, model, or dataset covered by this License.
+   - **"Commercial Use"**: Any use intended for monetary or competitive purposes, including but not limited to reselling, offering paid services, or integrating the Work into paid platforms.
+   - **"You"**: Any individual or organization using the Work under this License.
+
+2. **Ownership**
+   - The Work, including all versions, derivatives, and modifications, is solely owned by Shikhar Jadav.
+
+3. **Permissions**
+   - You are granted non-commercial use of the Work, provided that you comply with all terms of this License.
+   - You may modify, reproduce, and distribute the Work **only** for non-commercial purposes.
+
+4. **Commercial Use**
+   - **Prohibited Without Permission**: Any commercial use of the Work, or its derivatives, requires explicit written approval from the Owner.
+   - **Fees for Commercial Use**: Entities approved for commercial use must agree to pay a fee determined by the Owner prior to use.
+
+5. **Restrictions on Competitive Use**
+   - The Work may not be used to create, train, or improve a competing product or service without prior written consent from the Owner.
+   - Violations of this clause may result in legal action and immediate revocation of usage rights.
+
+6. **Attribution**
+   - Any use of the Work must include proper attribution to Shikhar Jadav, prominently displayed in documentation, user interfaces, or promotional materials.
+
+7. **Warranty Disclaimer**
+   - The Work is provided "AS IS" without warranties of any kind. The Owner is not responsible for any damages or liabilities arising from its use.
+
+8. **Termination**
+   - Any violation of these terms will result in the automatic termination of this License. Upon termination, all copies of the Work must be destroyed.
+
+---
+
+### Applying the License to Your Work
+To include this License, attach the following boilerplate notice to your files:
+
+```plaintext
+Copyright 2024 Shikhar Jadav
+
+This Work is licensed under the Shikhar Jadav License, Version 1.0.
+Unauthorized commercial or competitive use is strictly prohibited.
+See [https://github.com/ShikharHi/Licence-/blob/597cfc2f07411228dba4f322951fcf01cd2b0068/LICENSE.md] for full terms and conditions.
+``

README.md

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+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-1.5B
+tags:
+- chat
+library_name: transformers
+---
+
+# Qwen2.5-1.5B-Instruct
+
+## Introduction
+
+Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
+
+- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
+- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
+- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
+- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. 
+
+**This repo contains the instruction-tuned 1.5B Qwen2.5 model**, which has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining & Post-training
+- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
+- Number of Parameters: 1.54B
+- Number of Paramaters (Non-Embedding): 1.31B
+- Number of Layers: 28
+- Number of Attention Heads (GQA): 12 for Q and 2 for KV
+- Context Length: Full 32,768 tokens and generation 8192 tokens
+
+For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
+
+## Requirements
+
+The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
+
+With `transformers<4.37.0`, you will encounter the following error:
+```
+KeyError: 'qwen2'
+```
+
+## Quickstart
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Qwen/Qwen2.5-1.5B-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+
+## Evaluation & Performance
+
+Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
+
+For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@misc{qwen2.5,
+    title = {Qwen2.5: A Party of Foundation Models},
+    url = {https://qwenlm.github.io/blog/qwen2.5/},
+    author = {Qwen Team},
+    month = {September},
+    year = {2024}
+}
+
+@article{qwen2,
+      title={Qwen2 Technical Report}, 
+      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
+      journal={arXiv preprint arXiv:2407.10671},
+      year={2024}
+}
+```