Instructions to use Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16 with Transformers:

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

pipe = pipeline("text-generation", model="Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16")
model = AutoModelForMultimodalLM.from_pretrained("Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16")
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 Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16

SGLang

How to use Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16 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 "Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16" \
    --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": "Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16",
		"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 "Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16" \
        --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": "Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16 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 Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16 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 Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16",
    max_seq_length=2048,
)

Docker Model Runner
How to use Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16 with Docker Model Runner:
```
docker model run hf.co/Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Uploaded model

Developed by: Dragneel
License: apache-2.0
Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit

Use The Model

from transformers import AutoTokenizer, AutoModelForCausalLM

Load the tokenizer and model

tokenizer = AutoTokenizer.from_pretrained("Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16")

model = AutoModelForCausalLM.from_pretrained("Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16")

Example input text

input_text = "Summarize Nepali Text in Nepali: काठमाडौंको बहिराव बसपार्कमा एक भयानक दुर्घटना घटेको थियो। रातको समय थियो र भारी बर्फ जम्मा भएको थियो।"

Tokenize the input text

input_ids = tokenizer.encode(input_text, return_tensors='pt')

Generate text with adjusted parameters

outputs = model.generate(input_ids, max_new_tokens=50)

Decode the generated tokens

generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)

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Model tree for Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16

Base model

unsloth/Phi-3-mini-4k-instruct-bnb-4bit

Finetuned

(676)

this model

Dragneel
/

Phi-3-mini-Nepali-Text-Summarization-f16

Uploaded model

Use The Model

Model tree for Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16

Dataset used to train Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16