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)

# pip install -U transformers accelerate
# 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

llama.cpp

How to use Dibachain/Diba-Base with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Dibachain/Diba-Base:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dibachain/Diba-Base:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Dibachain/Diba-Base:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dibachain/Diba-Base:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Dibachain/Diba-Base:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Dibachain/Diba-Base:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Dibachain/Diba-Base:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dibachain/Diba-Base:Q4_K_M

Use Docker

docker model run hf.co/Dibachain/Diba-Base:Q4_K_M

LM Studio
Jan

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:Q4_K_M

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?"
			}
		]
	}'

Ollama
How to use Dibachain/Diba-Base with Ollama:
```
ollama run hf.co/Dibachain/Diba-Base:Q4_K_M
```
Unsloth Desktop

How to use Dibachain/Diba-Base with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dibachain/Diba-Base:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Dibachain/Diba-Base:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Dibachain/Diba-Base with Docker Model Runner:
```
docker model run hf.co/Dibachain/Diba-Base:Q4_K_M
```

Lemonade

How to use Dibachain/Diba-Base with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dibachain/Diba-Base:Q4_K_M

Run and chat with the model

lemonade run user.Diba-Base-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Dibachain/Diba-Base with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dibachain/Diba-Base:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Dibachain/Diba-Base:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Dibachain/Diba-Base with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dibachain/Diba-Base:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Dibachain/Diba-Base:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

CPU inference throughput at 8k context and recurrent hybrid scaling

by AndrewThompson1233 - opened Sep 16

Discussion

AndrewThompson1233

Sep 16

Hi Dibachain team,

Impressive work shipping a Persian-first 4B foundation model optimized for offline CPU deployment via GGUF and llama.cpp.

Given your focus on local CPU execution with 8,192 context lengths:
Token generation on CPUs is strictly memory-bandwidth bound (typically 40-80 GB/s on DDR4/DDR5 channels).
In standard full-attention or dense GQA backbones, the KV cache footprint at 8k context causes significant memory bus stalls, leading to noticeable generation slowdowns as context fills up.

In an open architecture project called Maba (101M reference model: https://huggingface.co/AndrewThompson1233/maba-v1-architecture), we address this compute profile using a hybrid recurrent design:
75% linear recurrent layers (GDN-2) paired with 25% grouped-query attention.
Linear recurrent layers maintain a fixed O(1) state per layer regardless of sequence length.
On CPU architectures, this cuts memory bandwidth pressure by roughly 70% at 8k context, keeping decoding speeds flat instead of degrading over long documents or tool-calling traces.

Additionally, if your Persian tokenizer uses an expanded vocabulary to maintain high fertility on ZWNJ and Arabic script, factorizing the embedding table through a low-rank bottleneck recovers significant parameter budget to keep the GGUF footprint within tight L3/RAM constraints.

What attention mechanism does the custom Diba architecture use under the hood, and how flat does your CPU token generation speed remain from 1k out to 8k context?

Best,
Andrew

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