Instructions to use HelpingAI/Dhanishtha-2.0-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HelpingAI/Dhanishtha-2.0-preview with Transformers:

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

pipe = pipeline("text-generation", model="HelpingAI/Dhanishtha-2.0-preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HelpingAI/Dhanishtha-2.0-preview")
model = AutoModelForCausalLM.from_pretrained("HelpingAI/Dhanishtha-2.0-preview")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HelpingAI/Dhanishtha-2.0-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HelpingAI/Dhanishtha-2.0-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HelpingAI/Dhanishtha-2.0-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HelpingAI/Dhanishtha-2.0-preview

SGLang

How to use HelpingAI/Dhanishtha-2.0-preview 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 "HelpingAI/Dhanishtha-2.0-preview" \
    --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": "HelpingAI/Dhanishtha-2.0-preview",
		"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 "HelpingAI/Dhanishtha-2.0-preview" \
        --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": "HelpingAI/Dhanishtha-2.0-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HelpingAI/Dhanishtha-2.0-preview with Docker Model Runner:
```
docker model run hf.co/HelpingAI/Dhanishtha-2.0-preview
```

Discrepancy in recommended sampling params

by owao - opened Jul 11, 2025

Discussion

owao

Jul 11, 2025

generation_config.json

{
  "do_sample": true,
  "temperature": 0.6,
  "top_k": 20,
  "top_p": 0.95,
}

}

Modelcard

generation_config = {
    "temperature": 0.7,
    "top_p": 0.9, 
    "top_k": 40,
    "max_new_tokens": 2048,
    "do_sample": True,
    "repetition_penalty": 1.1
}

Which ones to pick?

Thanks!

owao

Jul 11, 2025

•

edited Jul 11, 2025

I personally get way better instruction following using temp, top_k, and top_p from generation_config.json than the model card.
I set the remaining repetition_penaltyto 1.1as per the model card.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment