Instructions to use deadbear34/qwen35-4b-plantdisease-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deadbear34/qwen35-4b-plantdisease-merged with Transformers:

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

pipe = pipeline("text-generation", model="deadbear34/qwen35-4b-plantdisease-merged")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("deadbear34/qwen35-4b-plantdisease-merged")
model = AutoModelForMultimodalLM.from_pretrained("deadbear34/qwen35-4b-plantdisease-merged", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deadbear34/qwen35-4b-plantdisease-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deadbear34/qwen35-4b-plantdisease-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deadbear34/qwen35-4b-plantdisease-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deadbear34/qwen35-4b-plantdisease-merged

SGLang

How to use deadbear34/qwen35-4b-plantdisease-merged 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 "deadbear34/qwen35-4b-plantdisease-merged" \
    --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": "deadbear34/qwen35-4b-plantdisease-merged",
		"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 "deadbear34/qwen35-4b-plantdisease-merged" \
        --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": "deadbear34/qwen35-4b-plantdisease-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deadbear34/qwen35-4b-plantdisease-merged with Docker Model Runner:
```
docker model run hf.co/deadbear34/qwen35-4b-plantdisease-merged
```

Qwen3.5-4B Plant Disease — Merged Model

This is a merged model: Continued Pre-Training (CPT) base + SFT LoRA adapter combined into single weights.

Provenance

Base CPT: deadbear34/qwen35-4b-plantdisease-cpt
LoRA SFT: deadbear34/qwen35-4b-plantdisease-sft-lora
Tokenizer source: Qwen/Qwen3.5-4B-Base (original Qwen, vocab=248,044)
Merged at: 2026-05-13 14:32

Architecture

Base: Qwen3.5-4B with GatedDeltaNet hybrid architecture
Parameters: ~4.54B (fp16, single safetensors file)
Languages: English, Indonesian
Specialization: Plant disease diagnostics, agricultural advice

Usage

from transformers import AutoTokenizer, AutoModelForImageTextToText
import torch

model_id = "deadbear34/qwen35-4b-plantdisease-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="cuda",
    trust_remote_code=True,
)

prompt = "<|user|>
Apa penyebab late blight pada tomat?
<|assistant|>
"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Benchmark Results (STANDARD mode evaluation)

Benchmark	Score	Notes
GSM8K (n=300)	90.00%	Math reasoning with thinking format
MMLU (12 subjects, n=1000)	76.00%	Biology/medical-skewed subset
HumanEval (full 164)	42.68%	Coding (regressed from base)
TruthfulQA-MC1 (n=300)	57.00%	Truthfulness
PlantDisease ROUGE-1 (n=100)	38.39%	Domain-specific, EN+ID

⚠️ Note: MMLU evaluated on biology-heavy subset (12/57 subjects). Full MMLU likely lower.

License

Apache 2.0

Downloads last month: 7

Safetensors

Model size

5B params

Tensor type

BF16

Model tree for deadbear34/qwen35-4b-plantdisease-merged

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

deadbear34/qwen35-4b-plantdisease-cpt

Finetuned

(1)

this model