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 tokenizer.json from Dibachain/Diba-Base: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/Dibachain/Diba-Base/resolve/main/tokenizer.json
- Command line
-
hf download hf://Dibachain/Diba-Base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Dibachain/Diba-Base/resolve/main/tokenizer.json
20 MB
- Xet hash:
- a534f7c9d12bb01a2bc21781b55369e077043baed4c8f646fdeff5ff02dfd4d6
- Size of remote file:
- 20 MB
- SHA256:
- 6f32ce20dc35f57a7f9ad1eac03525bd7d30f9df8cea6507e958279cc3657706
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