Instructions to use yashAI007/Bhagavat_Gita with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yashAI007/Bhagavat_Gita with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "yashAI007/Bhagavat_Gita")

Transformers

How to use yashAI007/Bhagavat_Gita with Transformers:

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

pipe = pipeline("text-generation", model="yashAI007/Bhagavat_Gita")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("yashAI007/Bhagavat_Gita", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yashAI007/Bhagavat_Gita with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yashAI007/Bhagavat_Gita"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yashAI007/Bhagavat_Gita",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yashAI007/Bhagavat_Gita

SGLang

How to use yashAI007/Bhagavat_Gita 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 "yashAI007/Bhagavat_Gita" \
    --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": "yashAI007/Bhagavat_Gita",
		"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 "yashAI007/Bhagavat_Gita" \
        --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": "yashAI007/Bhagavat_Gita",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use yashAI007/Bhagavat_Gita 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 yashAI007/Bhagavat_Gita 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 yashAI007/Bhagavat_Gita to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yashAI007/Bhagavat_Gita to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="yashAI007/Bhagavat_Gita",
    max_seq_length=2048,
)

Docker Model Runner
How to use yashAI007/Bhagavat_Gita with Docker Model Runner:
```
docker model run hf.co/yashAI007/Bhagavat_Gita
```

Model Card for Model ID

Model Details

Base Model

unsloth/Llama-3.2-3B-Instruct

Features

Fine-tuned using Unsloth
4-bit quantization support
PEFT / LoRA adapter
Fast inference support
Optimized for Colab and consumer GPUs

Installation & Guidance for Inference

!pip install -q unsloth

# Install latest Unsloth
!pip install -q --force-reinstall --no-cache-dir git+https://github.com/unslothai/unsloth.git

!pip install --upgrade torchao>=0.16.0

%%capture
import os

if "COLAB_" not in "".join(os.environ.keys()):
    !pip install unsloth
else:
    !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl==0.15.2 triton cut_cross_entropy unsloth_zoo
    !pip install sentencepiece protobuf "datasets>=3.4.1" huggingface_hub hf_transfer
    !pip install --no-deps unsloth

from unsloth import FastLanguageModel
import torch

max_seq_length = 2048
dtype = None
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Llama-3.2-3B-Instruct",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

model.load_adapter("YOUR_LORA_PATH", "default")

model.to("cuda")

model.enable_adapters()

messages = [
    {
        "role": "system",
        "content": "You are a reflective assistant who is master in Bhagavad Gita for chapter 8."
    },
    {
        "role": "user",
        "content": "What does Krishna call Karma in this chapter?"
    }
]

FastLanguageModel.for_inference(model)

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True,
    return_tensors = "pt",
).to("cuda")

from transformers import TextStreamer

text_streamer = TextStreamer(tokenizer)

_ = model.generate(
    input_ids = inputs,
    streamer = text_streamer,
    max_new_tokens = 2048,
    do_sample = True,
    temperature = 0.3,
    top_p = 0.9
)

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