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", device_map="auto")
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 Settings

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
```

prithivMLmods commited on May 11, 2025

Commit

cb3e849

verified ·

1 Parent(s): 1e1c300

Update README.md

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@@ -22,29 +22,29 @@ datasets:
 # Ophiuchi-Qwen3-14B-Instruct
-> Ophiuchi-Qwen3-14B-Instruct is built upon the Qwen3-14B architecture, featuring Qwen3ForCausalLM and optimized for high-performance mathematical reasoning, coding proficiency, and factual accuracy. This model extends the capabilities of large instruction-tuned models by integrating precision-focused datasets, advanced reasoning chains, and fine-grained instruction alignment.
-Key enhancements include:
-1. Precision in Mathematical and Logical Reasoning
-   The model excels at solving complex mathematical problems, symbolic logic, and step-by-step derivations.
-2. Robust Code Understanding and Generation
-   Tuned for code generation and interpretation across multiple programming languages with an emphasis on correctness and structure.
-3. Accurate Factual Reasoning
-   Designed to reduce hallucination, this model prioritizes reliable and verifiable knowledge across domains.
 4. Long-Context Support
-   Handles up to 128K tokens in the input with support for 8K-token outputs, enabling comprehensive multi-step responses.
-5. Instruction-Tuned with High Fidelity
-   Responds precisely to multi-turn and nested instructions, maintaining structured and relevant outputs.
-6. Multilingual Competence
-   Supports over 29 global languages, facilitating international deployment and multilingual content creation.
-# Quickstart with Transformers
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -58,7 +58,8 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-prompt = "What are the core principles behind large language model alignment?"
 messages = [
     {"role": "system", "content": "You are a highly capable assistant focused on reasoning, coding, and factual precision."},
     {"role": "user", "content": prompt}
@@ -84,19 +85,26 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
 ```
-# Intended Use
-* Advanced mathematical and logical problem-solving
-* Code generation and debugging in multiple languages
-* Technical documentation and instruction parsing
-* Structured outputs (e.g., tables, JSON, config files)
-* Factual Q\&A and explanation tasks
-* Educational support and multilingual tutoring
-# Limitations
-* Requires high-memory hardware for optimal performance
-* May still produce factual inaccuracies on niche or adversarial prompts
-* Sensitive to prompt phrasing; structured prompts yield better results
-* Long outputs may propagate initial reasoning flaws
-* Creative tasks (e.g., fiction writing) may be less consistent

 # 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
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 )
 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}
 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. Saxton, D., Grefenstette, E., Hill, F., & Blunsom, P. (2019). Analysing Mathematical Reasoning Abilities of Neural Models. arXiv:1904.01557. [https://arxiv.org/pdf/1904.01557](https://arxiv.org/pdf/1904.01557)
+2. Chen, X., Zheng, S., & Liu, Z. (2023). YaRN: Efficient Context Window Extension of Large Language Models. arXiv:2309.00071. [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)