Text Generation
Transformers
Safetensors
Hebrew
English
duchifat_v2
chemistry
biology
finance
legal
music
art
code
climate
medical
agent
text-generation-inference
Duchifat-2
conversational
chat
SFT
custom_code
Instructions to use razielAI/Duchifat-2.2-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use razielAI/Duchifat-2.2-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="razielAI/Duchifat-2.2-Instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("razielAI/Duchifat-2.2-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use razielAI/Duchifat-2.2-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "razielAI/Duchifat-2.2-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "razielAI/Duchifat-2.2-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/razielAI/Duchifat-2.2-Instruct
- SGLang
How to use razielAI/Duchifat-2.2-Instruct 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 "razielAI/Duchifat-2.2-Instruct" \ --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": "razielAI/Duchifat-2.2-Instruct", "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 "razielAI/Duchifat-2.2-Instruct" \ --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": "razielAI/Duchifat-2.2-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use razielAI/Duchifat-2.2-Instruct with Docker Model Runner:
docker model run hf.co/razielAI/Duchifat-2.2-Instruct
| from transformers import PretrainedConfig | |
| class DuchifatConfig(PretrainedConfig): | |
| model_type = "duchifat_v2" | |
| def __init__( | |
| self, | |
| vocab_size=50257, | |
| hidden_size=768, | |
| num_layers=12, | |
| nhead=12, | |
| max_seq=1024, | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_layers = num_layers | |
| self.nhead = nhead | |
| self.max_seq = max_seq |