Instructions to use harshraj21/ekheti-advisory-qwen3-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use harshraj21/ekheti-advisory-qwen3-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B")
model = PeftModel.from_pretrained(base_model, "harshraj21/ekheti-advisory-qwen3-lora")

Transformers

How to use harshraj21/ekheti-advisory-qwen3-lora with Transformers:

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

pipe = pipeline("text-generation", model="harshraj21/ekheti-advisory-qwen3-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("harshraj21/ekheti-advisory-qwen3-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use harshraj21/ekheti-advisory-qwen3-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "harshraj21/ekheti-advisory-qwen3-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "harshraj21/ekheti-advisory-qwen3-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/harshraj21/ekheti-advisory-qwen3-lora

SGLang

How to use harshraj21/ekheti-advisory-qwen3-lora 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 "harshraj21/ekheti-advisory-qwen3-lora" \
    --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": "harshraj21/ekheti-advisory-qwen3-lora",
		"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 "harshraj21/ekheti-advisory-qwen3-lora" \
        --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": "harshraj21/ekheti-advisory-qwen3-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use harshraj21/ekheti-advisory-qwen3-lora with Docker Model Runner:
```
docker model run hf.co/harshraj21/ekheti-advisory-qwen3-lora
```

eKheti Agricultural Advisory LoRA

LoRA adapter fine-tuned from Qwen/Qwen3-0.6B for concise agricultural advisory responses. It is an experimental project artifact and is not the current production generator; production eKheti uses Qwen/Qwen3-4B-Instruct-2507 with official-document RAG, weather context, and deterministic safety checks.

Intended Use

Research and local demonstration of agriculture-oriented instruction tuning. Load this adapter with the declared base model through PEFT.

Limitations and Safety

The adapter does not contain live weather, market prices, official-document retrieval, or guaranteed current pesticide guidance. Never use its output alone for chemical selection, dosage, irrigation, or food-safety decisions. Pair it with retrieval, source citation, uncertainty handling, and local expert verification.

Training

The adapter was trained with the eKheti advisory LoRA pipeline. See training_config.json for recorded configuration. No claim of agronomist-level or field-validated accuracy is made.

Loading

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen3-0.6B"
tokenizer = AutoTokenizer.from_pretrained("harshraj21/ekheti-advisory-qwen3-lora")
model = AutoModelForCausalLM.from_pretrained(base)
model = PeftModel.from_pretrained(model, "harshraj21/ekheti-advisory-qwen3-lora")

Generated Training Metadata

Model Details

Model Description

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Uses

Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Model Architecture and Objective

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Framework versions

PEFT 0.19.1

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Paper for harshraj21/ekheti-advisory-qwen3-lora

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 59