Instructions to use KissanAI/ThinkingDhenu1-CRSA-India-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KissanAI/ThinkingDhenu1-CRSA-India-preview with Transformers:

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

pipe = pipeline("text-generation", model="KissanAI/ThinkingDhenu1-CRSA-India-preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KissanAI/ThinkingDhenu1-CRSA-India-preview")
model = AutoModelForCausalLM.from_pretrained("KissanAI/ThinkingDhenu1-CRSA-India-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 Settings

vLLM

How to use KissanAI/ThinkingDhenu1-CRSA-India-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KissanAI/ThinkingDhenu1-CRSA-India-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": "KissanAI/ThinkingDhenu1-CRSA-India-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KissanAI/ThinkingDhenu1-CRSA-India-preview

SGLang

How to use KissanAI/ThinkingDhenu1-CRSA-India-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 "KissanAI/ThinkingDhenu1-CRSA-India-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": "KissanAI/ThinkingDhenu1-CRSA-India-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 "KissanAI/ThinkingDhenu1-CRSA-India-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": "KissanAI/ThinkingDhenu1-CRSA-India-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KissanAI/ThinkingDhenu1-CRSA-India-preview with Docker Model Runner:
```
docker model run hf.co/KissanAI/ThinkingDhenu1-CRSA-India-preview
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for ThinkingDhenu1-CRSA-India-preview

This is an experimental research preview of a reasoning-augmented climate-smart agriculture (CRSA) model for Indian Agriculture.

Model Details


Developed by	KissanAI (https://kissan.ai)
Base model	`Qwen/Qwen3-4B`
Architecture	Qwen3 decoder-only causal-LM, 32 k context
Fine-tuning method	Supervised fine-tuning (SFT) via llama-factory
Languages	Primarily English + technical Indian-agricultural vocabulary
License	Apache 2.0 (inherits from base model)

Intended Use

Primary purpose
Assist farmers, agronomists and ag-tech developers with Climate-Resilient and Sustainable Agriculture (CRSA) recommendations tailored to Indian conditions (e.g., APCNF/organic practices, climate-smart cropping, pest IPM, soil/nutrient management).

Direct use examples

Decision-support micro-service answering agronomic queries.
Content generation for ag-extension material.

Out-of-scope uses

Any medical, legal, or financial advice.
Real-time critical decision making without human validation.
Disinformation, hateful or extremist content.

Training Data

Dataset	Size	Notes
`KissanAI/Thinking-climate-100k`	101 k multi-turn dialogues on climate-smart ag topics with thinking tags

The dataset is synthetic/aligned through “chain-of-thought + answer” format that explicitly separates the model’s private reasoning (<think> … </think>) from the final answer, reducing chain-of-thought leakage at inference time.

Bias, Risks & Limitations

May embed agronomic bias toward Indian Natural Farming practices (APCNF).
Climate data cited is static (2024) – cross-check against latest IMD advisories.
Still prone to LLM hallucinations; always validate high-stakes advice with qualified professionals.

Citation

@misc{KissanAI2025ThinkingDhenu1,
  title        = {ThinkingDhenu1-CRSA-India-preview},
  author       = {KissanAI},
  howpublished = {\url{https://huggingface.co/KissanAI/ThinkingDhenu1-CRSA-India-preview}},
  year         = {2025},
  note         = {Fine-tuned from Qwen3-4B on Indian climate-smart agriculture data.}
}