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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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base_model: |
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- Qwen/Qwen3-14B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- code |
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- math |
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- moe |
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datasets: |
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- open-r1/OpenR1-Math-220k |
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- deepmind/math_dataset |
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- burtenshaw/tulu-3-sft-personas-code-no-prompt |
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--- |
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# Ophiuchi-Qwen3-14B-Instruct |
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> Ophiuchi-Qwen3-14B-Instruct is built upon the Qwen3-14B architecture and uses the Qwen3ForCausalLM backbone. It is instruction-tuned to enhance capabilities in mathematical reasoning, code generation, and factual accuracy. By leveraging high-quality datasets and long-context architectures, this model is designed to excel in solving complex reasoning tasks and generating accurate, structured content across multiple domains. |
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## Key Features |
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1. Mathematical and Logical Reasoning |
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Fine-tuned to perform step-by-step reasoning, symbolic logic, and advanced mathematics, supporting educational and technical use cases. |
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2. Code Generation and Understanding |
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Optimized for writing, interpreting, and debugging code across various programming languages, including Python, JavaScript, and C++. |
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3. Factual Integrity and Precision |
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Trained on curated and aligned datasets to enhance accuracy and reduce hallucination in fact-based tasks. |
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4. Long-Context Support |
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Capable of handling up to 128K tokens as input with output generation up to 8K tokens, enabling detailed and comprehensive responses over extended sequences. |
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5. Instruction-Tuned Alignment |
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Demonstrates a strong ability to follow multi-step instructions, maintain conversation context, and produce structured outputs across sessions. |
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6. Multilingual Proficiency |
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Supports over 29 languages including English, Chinese, French, Spanish, Arabic, Russian, Japanese, Korean, and others, enabling global communication and translation tasks. |
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## Quickstart with Transformers |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Ophiuchi-Qwen3-14B-Instruct" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Explain the principles of alignment in large language models." |
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messages = [ |
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{"role": "system", "content": "You are a highly capable assistant focused on reasoning, coding, and factual precision."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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## Intended Use |
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* Mathematical and symbolic problem solving |
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* Code generation and explanation |
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* Structured response generation in JSON, Markdown, or table formats |
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* Long-form technical writing and documentation |
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* Factual question answering and fact-checking |
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* Educational assistance across STEM domains |
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* Multilingual conversation and translation tasks |
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## Limitations |
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* High computational requirements (A100/H100-class GPUs recommended) |
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* May still produce hallucinated facts on edge cases or adversarial inputs |
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* Sensitive to poorly structured or ambiguous prompts |
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* Early-stage errors may propagate in long outputs |
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* Less suitable for creative fiction or subjective narrative tasks |
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## References |
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1. Analysing Mathematical Reasoning Abilities of Neural Models. arXiv:1904.01557. [https://arxiv.org/pdf/1904.01557](https://arxiv.org/pdf/1904.01557) |
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2. YaRN: Efficient Context Window Extension of Large Language Models. arXiv:2309.00071. [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071) |
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