Text Generation
Transformers
PyTorch
Arabic
English
jais
Arabic
English
LLM
Decoder
causal-lm
custom_code
Instructions to use inceptionai/jais-13b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inceptionai/jais-13b-chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inceptionai/jais-13b-chat", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("inceptionai/jais-13b-chat", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use inceptionai/jais-13b-chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inceptionai/jais-13b-chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inceptionai/jais-13b-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/inceptionai/jais-13b-chat
- SGLang
How to use inceptionai/jais-13b-chat 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 "inceptionai/jais-13b-chat" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inceptionai/jais-13b-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "inceptionai/jais-13b-chat" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inceptionai/jais-13b-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use inceptionai/jais-13b-chat with Docker Model Runner:
docker model run hf.co/inceptionai/jais-13b-chat
Updating `hidden_states` value in line 869
#14
by alielfilali01 - opened
This update is meant to solve the following error that occured when trying to finetune the model using peft :
RuntimeError Traceback (most recent call last)
in <cell line: 2>()
1 trainer.deprecated=True
----> 2 trainer.train()
21 frames
~/.cache/huggingface/modules/transformers_modules/inception-mbzuai/jais-13b-chat/96080d1c163804428c4792b8618c2d39661e9d7f/modeling_jais.py in forward(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, use_cache, output_attentions, output_hidden_states, return_dict)
867 else:
868 hidden_states = inputs_embeds
→ 869 hidden_states *= torch.tensor(
870 float(self.embeddings_scale), dtype=hidden_states.dtype, device=hidden_states.device
871 )
RuntimeError: a leaf Variable that requires grad is being used in an in-place operation.
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