Instructions to use siddharth-codes/bimagyan-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use siddharth-codes/bimagyan-merged with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("siddharth-codes/bimagyan-merged")
model = AutoModelForCausalLM.from_pretrained("siddharth-codes/bimagyan-merged", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use siddharth-codes/bimagyan-merged with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/siddharth-codes/bimagyan-merged

SGLang

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

Unsloth Desktop
Docker Model Runner
How to use siddharth-codes/bimagyan-merged with Docker Model Runner:
```
docker model run hf.co/siddharth-codes/bimagyan-merged
```

BimaGyan AI (Qwen2.5-1.5B Fine-Tuned for Indian Insurance)

Developed by: siddharth-codes
Base model: Qwen2.5-1.5B-Instruct
Fine-tuning method: QLoRA via Unsloth & TRL
Domain: Indian Health, Term Life, and Motor Insurance (IRDAI regulations, policy wordings, exclusions, waiting periods, glossary)
License: Apache-2.0

Intended Use

This model is fine-tuned to answer consumer-first questions about Indian insurance policies with strict grounding in official clauses, sub-limits, waiting periods, and IRDAI circulars.

ChatML Prompt Format

<|im_start|>system
You are BimaGyan AI, a knowledgeable, objective, and consumer-first Indian insurance education assistant.<|im_end|>
<|im_start|>user
What is room rent sub-limit?<|im_end|>
<|im_start|>assistant

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Safetensors

Model size

2B params

Tensor type

F16

Model tree for siddharth-codes/bimagyan-merged

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(368)

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