Instructions to use Dibachain/Diba-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dibachain/Diba-Base with Transformers:

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

pipe = pipeline("text-generation", model="Dibachain/Diba-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Dibachain/Diba-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dibachain/Diba-Base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Dibachain/Diba-Base

SGLang

How to use Dibachain/Diba-Base 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 "Dibachain/Diba-Base" \
    --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": "Dibachain/Diba-Base",
		"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 "Dibachain/Diba-Base" \
        --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": "Dibachain/Diba-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Dibachain/Diba-Base with Docker Model Runner:
```
docker model run hf.co/Dibachain/Diba-Base
```

Diba-Base / NOTICE

DibaAi

Release

0e89952 15 days ago

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	Diba-Base
	Copyright 2026 Dibachain (dibachain.ir)

	This product is licensed under the Apache License, Version 2.0 (see LICENSE).

	This model is a derivative work. It was created by further training the following
	open-weight model, released under the Apache License, Version 2.0:

	Qwen3.5-4B
	Copyright (c) Alibaba Cloud
	https://huggingface.co/Qwen/Qwen3.5-4B

	Modifications by Dibachain: supervised fine-tuning on Persian, Iranian-history,
	company and software-development data; merged adapter weights; GGUF quantization.

	Training data attributions:
	- Persian Wikipedia content (CC BY-SA 4.0), rewritten into question/answer form.
	- Open code instruction datasets: OpenCoder opc-sft (MIT), Magicoder OSS-Instruct (MIT),
	Magicoder Evol-Instruct (Apache 2.0), glaive-code-assistant-v3 (Apache 2.0),
	CodeFeedback-Filtered-Instruction (Apache 2.0), McEval-Instruct (Apache 2.0),
	nvidia/OpenCodeInstruct (CC-BY-4.0).