Instructions to use JDhruv14/Qwen2.5-3B-Gita-FT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JDhruv14/Qwen2.5-3B-Gita-FT with Transformers:

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

pipe = pipeline("text-generation", model="JDhruv14/Qwen2.5-3B-Gita-FT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("JDhruv14/Qwen2.5-3B-Gita-FT")
model = AutoModelForMultimodalLM.from_pretrained("JDhruv14/Qwen2.5-3B-Gita-FT")
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 JDhruv14/Qwen2.5-3B-Gita-FT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JDhruv14/Qwen2.5-3B-Gita-FT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JDhruv14/Qwen2.5-3B-Gita-FT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JDhruv14/Qwen2.5-3B-Gita-FT

SGLang

How to use JDhruv14/Qwen2.5-3B-Gita-FT 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 "JDhruv14/Qwen2.5-3B-Gita-FT" \
    --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": "JDhruv14/Qwen2.5-3B-Gita-FT",
		"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 "JDhruv14/Qwen2.5-3B-Gita-FT" \
        --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": "JDhruv14/Qwen2.5-3B-Gita-FT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use JDhruv14/Qwen2.5-3B-Gita-FT 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 JDhruv14/Qwen2.5-3B-Gita-FT 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 JDhruv14/Qwen2.5-3B-Gita-FT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for JDhruv14/Qwen2.5-3B-Gita-FT to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="JDhruv14/Qwen2.5-3B-Gita-FT",
    max_seq_length=2048,
)

Docker Model Runner
How to use JDhruv14/Qwen2.5-3B-Gita-FT with Docker Model Runner:
```
docker model run hf.co/JDhruv14/Qwen2.5-3B-Gita-FT
```

Qwen2.5-3B-Gita-FT

Dataset Banner

A Bhagavad Gita–focused assistant that adopts a Krishna-inspired teaching persona for guiding in your spiritual path.

🌟 Model Description

Qwen2.5-3B-Gita-FT is a LoRA-tuned model built on Qwen/Qwen2.5-3B-Instruct, focused on tasks around the Bhagavad Gītā. It supports:

Krishna-inspired persona: Calm, compassionate, and practical tone for guidance and teaching.
Commentary Q&A — approachable explanations of concepts (e.g., niṣkāma-karma, guṇa theory), in a Krishna-like tone.

Important: The model is not Krishna, nor a religious authority. It patterns its style from training data and prompts. It can make mistakes, simplify nuanced ideas, misremember verse numbers, or produce non-canonical wording. For study or citation, please verify with authoritative editions and scholars.

🚀 Key Features

Commentary tone control: System prompts steer classical or modern explanatory style.
Resource efficient: LoRA adapters with mixed precision; optional 4-bit inference.

📊 Model Specs

Parameter	Value
Base Model	Qwen/Qwen2.5-3B-Instruct
Fine-tuning	LoRA (rank=16, alpha=32)
Seq Length	1024 (recommend ≥ 512 for long verses)
Epochs	3
LR	2e-4
Batch	2 (micro) × 4 (grad acc)
Optimizer	AdamW 8-bit
Precision	bf16 (training & inference where available)

🎯 Intended Uses

✅ Recommended

Study aids for verse comprehension, transliteration, and quick glosses.
Educational apps and assistive tools for learners.
Search & summarize experiences for specific verses and concepts.

⚠️ Limitations

Interpretation variance: Philosophical terms can have multiple valid readings.
Historical/cultural nuance: May miss context without retrieval.
Hallucinations: Makes a lot of mistakes while generating Hindi and Gujarati

🛠️ Quickstart (Transformers)

Requires transformers>=4.41, torch, accelerate. Some Qwen models need trust_remote_code=True.

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "JDhruv14/Qwen2.5-3B-Gita-FT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Prepare the conversation
messages = [
    {
        "role": "system", 
        "content": "You are Lord Krishna—the serene, compassionate teacher of the Bhagavad Gita."
    },
    {
        "role": "user", 
        "content": "Hey Keshav, what's my dharma?"
    }
]

# Apply chat template and generate
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)

📚 Citation

@misc{gita-qwen-assistant,
  title={Gita-qwen-3B-Assistan: A Bhagavad Gītā-focused model for motivating you, guiding you based on the eternal guidance of Madhav.},
  author={JDhruv14},
  year={2025},
  url={https://huggingface.co/JDhruv14/Qwen2.5-3B-Gita-FT}
}

🤝 Contributing

Add verse-aligned examples, domain-checked glosses, and evaluation sets.
Propose prompt templates for specific chapters/themes (e.g., Karma-yoga, Bhakti-yoga).
Open issues/PRs for bugs or inaccuracies.

📄 License

Released under Apache 2.0. See LICENSE.

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for JDhruv14/Qwen2.5-3B-Gita-FT

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct