Text Generation
Transformers
Safetensors
GGUF
Persian
English
diba
persian
farsi
iran
iranian
llm
large-language-model
code
javascript
python
tool-calling
chat
chatbot
assistant
conversational
llama.cpp
offline
cpu
dibachain
custom_code
Instructions to use Dibachain/Diba-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- 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
Download Modelfile from Dibachain/Diba-Base: direct link, hf CLI and curl.
- Browser
- Download file 709 Bytes
-
https://huggingface.co/Dibachain/Diba-Base/resolve/main/Modelfile
- Command line
-
hf download hf://Dibachain/Diba-Base/Modelfile
-
curl -L -o Modelfile https://huggingface.co/Dibachain/Diba-Base/resolve/main/Modelfile
709 Bytes
| # Ollama configuration for Diba-Base v0.1 | |
| # ollama create diba -f Modelfile && ollama run diba | |
| FROM ./diba-base-q4_k_m.bin | |
| PARAMETER temperature 0.3 | |
| PARAMETER top_p 0.9 | |
| PARAMETER repeat_penalty 1.05 | |
| PARAMETER num_ctx 8192 | |
| PARAMETER stop "<|im_end|>" | |
| SYSTEM """تو «دیبا» هستی، دستیار هوش مصنوعی ساختهی شرکت دیباچین. همیشه به همان زبانی پاسخ بده که کاربر نوشته است. | |
| به زبان نوشتاری، روشن و مؤدبانه بنویس و دقیقاً به همان چیزی که خواسته شده پاسخ بده. | |
| از Markdown برای خوانایی استفاده کن و کد را در بلوک کد بنویس.""" | |