Instructions to use sapkotapraful/answerme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sapkotapraful/answerme with Transformers:

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

pipe = pipeline("text-generation", model="sapkotapraful/answerme")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("sapkotapraful/answerme")
model = AutoModelForImageTextToText.from_pretrained("sapkotapraful/answerme")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sapkotapraful/answerme with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sapkotapraful/answerme

SGLang

How to use sapkotapraful/answerme 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 "sapkotapraful/answerme" \
    --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": "sapkotapraful/answerme",
		"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 "sapkotapraful/answerme" \
        --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": "sapkotapraful/answerme",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use sapkotapraful/answerme 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 sapkotapraful/answerme 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 sapkotapraful/answerme to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sapkotapraful/answerme to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="sapkotapraful/answerme",
    max_seq_length=2048,
)

Docker Model Runner
How to use sapkotapraful/answerme with Docker Model Runner:
```
docker model run hf.co/sapkotapraful/answerme
```

Fine-Tuned Gemma 4 Model

Developed by: sapkotapraful License: Apache-2.0 Base Model: unsloth/gemma-4-e2b-it-unsloth-bnb-4bit

Model Description

This model is a fine-tuned version of Gemma 4 trained for retrieval and reranking tasks. Given a search query and a collection of candidate passages, the model selects and returns the most relevant passage from the provided corpus.

The model was fine-tuned using Unsloth and Hugging Face's TRL library for efficient training.

Input Format

The model was trained on chat-formatted examples:

[
    {"role": "system", "content": "<|GET|>"},
    {
        "role": "user",
        "content": {
            "query": "most dependable affordable cars",
            "corpus": [
                "Document 1...",
                "Document 2...",
                "If you can look past its bargain interior and anonymous exterior, the Suzuki SX4 is one of the most reliable and affordable all-wheel-drive cars."
            ]
        }
    }
]

Output Format

The model returns the most relevant passage from the corpus:

If you can look past its bargain interior and anonymous exterior, the Suzuki SX4 is one of the most reliable and affordable all-wheel-drive cars.

Training Objective

Query-document relevance matching
Passage retrieval and reranking
Selection of the best matching document from a candidate set

Training Framework

Unsloth
Hugging Face Transformers
Hugging Face TRL

Acknowledgements

This model was fine-tuned using Unsloth for fast and memory-efficient training.

Downloads last month: 23

Safetensors

Model size

5B params

Tensor type

BF16