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
Download tokenizer_config.json from razielAI/Duchifat-2.2-Instruct: direct link, hf CLI and curl.
- Browser
- Download file 403 Bytes
-
https://huggingface.co/razielAI/Duchifat-2.2-Instruct/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://razielAI/Duchifat-2.2-Instruct/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/razielAI/Duchifat-2.2-Instruct/resolve/main/tokenizer_config.json
403 Bytes
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<s>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "</s>", | |
| "is_local": true, | |
| "legacy": true, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "spaces_between_special_tokens": false, | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<unk>", | |
| "use_default_system_prompt": false | |
| } | |