Instructions to use parkky21/orpheus-3b-hi-ft-1e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use parkky21/orpheus-3b-hi-ft-1e with Transformers:

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

pipe = pipeline("text-generation", model="parkky21/orpheus-3b-hi-ft-1e")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("parkky21/orpheus-3b-hi-ft-1e")
model = AutoModelForCausalLM.from_pretrained("parkky21/orpheus-3b-hi-ft-1e")
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 Settings

vLLM

How to use parkky21/orpheus-3b-hi-ft-1e with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "parkky21/orpheus-3b-hi-ft-1e"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "parkky21/orpheus-3b-hi-ft-1e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/parkky21/orpheus-3b-hi-ft-1e

SGLang

How to use parkky21/orpheus-3b-hi-ft-1e 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 "parkky21/orpheus-3b-hi-ft-1e" \
    --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": "parkky21/orpheus-3b-hi-ft-1e",
		"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 "parkky21/orpheus-3b-hi-ft-1e" \
        --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": "parkky21/orpheus-3b-hi-ft-1e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use parkky21/orpheus-3b-hi-ft-1e with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for parkky21/orpheus-3b-hi-ft-1e to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for parkky21/orpheus-3b-hi-ft-1e to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for parkky21/orpheus-3b-hi-ft-1e to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="parkky21/orpheus-3b-hi-ft-1e",
    max_seq_length=2048,
)

Docker Model Runner
How to use parkky21/orpheus-3b-hi-ft-1e with Docker Model Runner:
```
docker model run hf.co/parkky21/orpheus-3b-hi-ft-1e
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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parkky21/orpheus-3b-hi-duo-voices (अनुष्का • करन)

🔎 Model Summary

Base model: canopylabs/3b-hi-pretrain-research_release
Finetuned by: parkky21
Language: Hindi (hi), with Hinglish tolerance
Voices: अनुष्का (warm, curious), करन (friendly, direct)
Architecture: LLaMA-family, decoder-only
Intended use: Multi-turn dialogue in Hindi with lightweight “voice” control via speaker prefixes

▶️ Try It (Colab) Use the Colab notebook for inference and examples—no local setup needed: Colab: https://colab.research.google.com/drive/1-greyn4D7-0SVUx86fGPzj5rjB2DjGUn?usp=sharing

✨ What’s Special

Two natural voices out of the box—switch tone by prefixing lines with the speaker name.
Simple prompting (no special chat template required).
Fast + lightweight—great for laptops and mid-tier GPUs thanks to Unsloth and 3B size.

🗣️ Voices & Prompting

Use speaker-name prefixes followed by a colon. Example conversation style:

अनुष्का: हे करन, क्या आज बारिश ज़्यादा नहीं हो रही?
करन: हाँ, बहुत ज़्यादा! सुबह से रुकने का नाम ही नहीं ले रही।

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

F32

F16

Model tree for parkky21/orpheus-3b-hi-ft-1e

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

canopylabs/orpheus-3b-0.1-pretrained

Finetuned

canopylabs/3b-hi-pretrain-research_release

Finetuned

(32)

this model

Finetunes

3 models

Quantizations

1 model