Instructions to use prithivMLmods/Ophiuchi-Qwen3-14B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Ophiuchi-Qwen3-14B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Ophiuchi-Qwen3-14B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Ophiuchi-Qwen3-14B-Instruct")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Ophiuchi-Qwen3-14B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Ophiuchi-Qwen3-14B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Ophiuchi-Qwen3-14B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Ophiuchi-Qwen3-14B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Ophiuchi-Qwen3-14B-Instruct

SGLang

How to use prithivMLmods/Ophiuchi-Qwen3-14B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Ophiuchi-Qwen3-14B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Ophiuchi-Qwen3-14B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Ophiuchi-Qwen3-14B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Ophiuchi-Qwen3-14B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Ophiuchi-Qwen3-14B-Instruct with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Ophiuchi-Qwen3-14B-Instruct
```

Ophiuchi-Qwen3-14B-Instruct / README.md

prithivMLmods

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metadata

license: apache-2.0
language:
  - en
  - zh
base_model:
  - Qwen/Qwen3-14B
pipeline_tag: text-generation
library_name: transformers
tags:
  - text-generation-inference
  - code
  - math
  - moe
datasets:
  - open-r1/OpenR1-Math-220k
  - deepmind/math_dataset
  - burtenshaw/tulu-3-sft-personas-code-no-prompt

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

Mathematical and Logical Reasoning Fine-tuned to perform step-by-step reasoning, symbolic logic, and advanced mathematics, supporting educational and technical use cases.
Code Generation and Understanding Optimized for writing, interpreting, and debugging code across various programming languages, including Python, JavaScript, and C++.
Factual Integrity and Precision Trained on curated and aligned datasets to enhance accuracy and reduce hallucination in fact-based tasks.
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.
Instruction-Tuned Alignment Demonstrates a strong ability to follow multi-step instructions, maintain conversation context, and produce structured outputs across sessions.
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

Analysing Mathematical Reasoning Abilities of Neural Models. arXiv:1904.01557. https://arxiv.org/pdf/1904.01557
YaRN: Efficient Context Window Extension of Large Language Models. arXiv:2309.00071. https://arxiv.org/pdf/2309.00071