Instructions to use shehryars715/finetuned-llama-3.2-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shehryars715/finetuned-llama-3.2-3b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-3B-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "shehryars715/finetuned-llama-3.2-3b")

Transformers

How to use shehryars715/finetuned-llama-3.2-3b with Transformers:

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

pipe = pipeline("text-generation", model="shehryars715/finetuned-llama-3.2-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("shehryars715/finetuned-llama-3.2-3b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shehryars715/finetuned-llama-3.2-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shehryars715/finetuned-llama-3.2-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shehryars715/finetuned-llama-3.2-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shehryars715/finetuned-llama-3.2-3b

SGLang

How to use shehryars715/finetuned-llama-3.2-3b 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 "shehryars715/finetuned-llama-3.2-3b" \
    --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": "shehryars715/finetuned-llama-3.2-3b",
		"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 "shehryars715/finetuned-llama-3.2-3b" \
        --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": "shehryars715/finetuned-llama-3.2-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use shehryars715/finetuned-llama-3.2-3b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for shehryars715/finetuned-llama-3.2-3b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for shehryars715/finetuned-llama-3.2-3b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shehryars715/finetuned-llama-3.2-3b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="shehryars715/finetuned-llama-3.2-3b",
    max_seq_length=2048,
)

Docker Model Runner
How to use shehryars715/finetuned-llama-3.2-3b with Docker Model Runner:
```
docker model run hf.co/shehryars715/finetuned-llama-3.2-3b
```

🌾 Agricultural Advisory LLM — Llama-3.2-3B LoRA

A LoRA fine-tuned version of Llama-3.2-3B-Instruct for agricultural question answering and crop advisory generation. This is the 3B parameter variant — more capable than the 1B version for complex agronomic reasoning while remaining lightweight enough for edge deployment.

Fine-tuned using Unsloth for fast, memory-efficient training on a single GPU.

Also available: 1B variant (lighter, faster inference)

Model Details

Field	Details
Base model	`unsloth/Llama-3.2-3B-Instruct-bnb-4bit`
Fine-tuning method	LoRA (Low-Rank Adaptation) via PEFT
Trainer	SFTTrainer (TRL) + Unsloth
Precision	4-bit quantized (bitsandbytes)
Language	English
Domain	Agriculture / Crop Advisory
Developed by	shehryars715

Intended Use

✅ Direct Use

Answering crop-specific agricultural questions (pest control, irrigation, fertilization, disease management)
Generating advisories from remote sensing inputs (NDVI, EVI, crop growth stage)
Decision support for farmers, agronomists, and precision agriculture systems

✅ Downstream Use

Backend for agri-advisory chatbots and mobile apps
Integration into precision agriculture dashboards
RAG pipelines augmented with real-time field sensor data

❌ Out-of-Scope Use

Medical, legal, or financial advice
Non-agricultural domains
Autonomous high-stakes decisions without human expert review

Training Details

Training Data

Two datasets were mixed during fine-tuning:

General Agricultural Advisories — structured Q&A with fields: crop, topic, question, answer
Farm Sensor Data Advisories — precision agriculture records with fields: crop_type, stage, NDVI, EVI, and generated Advisory

Training Hyperparameters

Parameter	Value
Training regime	fp16 mixed precision
LoRA rank (r)	16
LoRA alpha	16
LoRA dropout	0
Target modules	q, k, v, o, gate, up, down proj
Batch size	2 (per device)
Gradient accumulation	4 steps (effective batch = 8)
Epochs	3
Learning rate	2e-4
Optimizer	AdamW 8-bit
LR scheduler	Linear
Max seq length	2048
Hardware	Kaggle (NVIDIA T4 / P100, single GPU)

How to Get Started

With Unsloth (recommended — fastest)

Downloads last month: 5

Model tree for shehryars715/finetuned-llama-3.2-3b

Base model

meta-llama/Llama-3.2-3B-Instruct

Quantized

unsloth/Llama-3.2-3B-Instruct-bnb-4bit

Adapter

(61)

this model