Ophiuchi-Qwen3-14B-Instruct

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.

Key Features

1. Mathematical and Logical Reasoning Fine-tuned to perform step-by-step reasoning, symbolic logic, and advanced mathematics, supporting educational and technical use cases.

2. Code Generation and Understanding Optimized for writing, interpreting, and debugging code across various programming languages, including Python, JavaScript, and C++.

3. Factual Integrity and Precision Trained on curated and aligned datasets to enhance accuracy and reduce hallucination in fact-based tasks.

4. Long-Context Support Capable of handling up to 128K tokens as input with output generation up to 8K tokens, enabling detailed and comprehensive responses over extended sequences.

5. Instruction-Tuned Alignment Demonstrates a strong ability to follow multi-step instructions, maintain conversation context, and produce structured outputs across sessions.

6. Multilingual Proficiency Supports over 29 languages including English, Chinese, French, Spanish, Arabic, Russian, Japanese, Korean, and others, enabling global communication and translation tasks.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Ophiuchi-Qwen3-14B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Explain the principles of alignment in large language models."

messages = [
    {"role": "system", "content": "You are a highly capable assistant focused on reasoning, coding, and factual precision."},
    {"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]
print(response)

Intended Use

• Mathematical and symbolic problem solving
• Code generation and explanation
• Structured response generation in JSON, Markdown, or table formats
• Long-form technical writing and documentation
• Factual question answering and fact-checking
• Educational assistance across STEM domains
• Multilingual conversation and translation tasks

Limitations

• High computational requirements (A100/H100-class GPUs recommended)
• May still produce hallucinated facts on edge cases or adversarial inputs
• Sensitive to poorly structured or ambiguous prompts
• Early-stage errors may propagate in long outputs
• Less suitable for creative fiction or subjective narrative tasks

References

1. Analysing Mathematical Reasoning Abilities of Neural Models. arXiv:1904.01557. https://arxiv.org/pdf/1904.01557

2. YaRN: Efficient Context Window Extension of Large Language Models. arXiv:2309.00071. https://arxiv.org/pdf/2309.00071