Instructions to use isubirsingh/predictram-vedaniq-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use isubirsingh/predictram-vedaniq-v1 with Transformers:

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

pipe = pipeline("text-generation", model="isubirsingh/predictram-vedaniq-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("isubirsingh/predictram-vedaniq-v1")
model = AutoModelForCausalLM.from_pretrained("isubirsingh/predictram-vedaniq-v1", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use isubirsingh/predictram-vedaniq-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/isubirsingh/predictram-vedaniq-v1

SGLang

How to use isubirsingh/predictram-vedaniq-v1 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 "isubirsingh/predictram-vedaniq-v1" \
    --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": "isubirsingh/predictram-vedaniq-v1",
		"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 "isubirsingh/predictram-vedaniq-v1" \
        --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": "isubirsingh/predictram-vedaniq-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use isubirsingh/predictram-vedaniq-v1 with Docker Model Runner:
```
docker model run hf.co/isubirsingh/predictram-vedaniq-v1
```

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PredictRAM Vedaniq is provided under controlled research access. Please submit your details for manual review. Access may be granted for research, evaluation, internal experimentation, or approved commercial discussion only. This model does not provide investment advice and must not be used as the sole basis for trading, investing, compliance, or financial decisions.

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