Instructions to use prithivMLmods/Wolf-Rayet-2B-Prime3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Wolf-Rayet-2B-Prime3 with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Wolf-Rayet-2B-Prime3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Wolf-Rayet-2B-Prime3")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Wolf-Rayet-2B-Prime3")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Wolf-Rayet-2B-Prime3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Wolf-Rayet-2B-Prime3"
# 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/Wolf-Rayet-2B-Prime3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Wolf-Rayet-2B-Prime3

SGLang

How to use prithivMLmods/Wolf-Rayet-2B-Prime3 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/Wolf-Rayet-2B-Prime3" \
    --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/Wolf-Rayet-2B-Prime3",
		"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/Wolf-Rayet-2B-Prime3" \
        --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/Wolf-Rayet-2B-Prime3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Wolf-Rayet-2B-Prime3 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Wolf-Rayet-2B-Prime3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Wolf-Rayet-2B-Prime3

Wolf-Rayet-2B-Prime3 is a compact, coding-optimized language model built on the Qwen3 1.7B architecture, fine-tuned for high-accuracy code generation, debugging, and technical reasoning. With approximately 2 billion effective parameters, it offers a strong balance between performance and deployability—ideal for developers, educators, and engineers operating in resource-constrained or latency-sensitive environments.

GGUF: https://huggingface.co/prithivMLmods/Wolf-Rayet-2B-Prime3-GGUF

Key Features

Qwen3 Architecture Core Based on the modern and efficient Qwen3 1.7B transformer backbone, offering improved context handling and token efficiency for both single-turn and multi-turn programming tasks.
Code-First Fine-Tuning Trained extensively on diverse code datasets including Python, JavaScript, C++, and Bash, with auxiliary tuning on software documentation, APIs, and debugging dialogues.
Multi-Step Technical Reasoning Demonstrates the ability to deconstruct complex programming problems, explain logic, refactor code, and correct errors—particularly useful for students, engineers, and coding educators.
Structured Output Proficiency Supports accurate generation of structured formats like JSON, YAML, Markdown, and code blocks—ready to plug into developer tools, notebooks, and documentation pipelines.
Compact Yet Capable With a ~2B parameter scale, it delivers competitive performance without the high resource requirements of larger models, and is easily deployable on modern GPUs or high-end CPUs.
Multilingual Coding Support Capable of generating and understanding code in 10+ programming languages, with a focus on real-world use cases, automation scripts, and algorithmic solutions.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Wolf-Rayet-2B-Prime3"

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

prompt = "Write a Python function to check if a number is prime."

messages = [
    {"role": "system", "content": "You are a helpful coding 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]
print(response)