Instructions to use rinna/nekomata-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rinna/nekomata-14b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rinna/nekomata-14b", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("rinna/nekomata-14b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rinna/nekomata-14b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rinna/nekomata-14b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rinna/nekomata-14b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rinna/nekomata-14b
- SGLang
How to use rinna/nekomata-14b 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 "rinna/nekomata-14b" \ --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": "rinna/nekomata-14b", "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 "rinna/nekomata-14b" \ --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": "rinna/nekomata-14b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rinna/nekomata-14b with Docker Model Runner:
docker model run hf.co/rinna/nekomata-14b
Differences between modeling_qwen.py in nekomata-14b and Qwen-14b Repositories
There appears to be a difference between the modeling_qwen.py file in the nekomata-14b repository and the one in the qwen-14b repository. You can find them at the following links:
https://huggingface.co/Qwen/Qwen-14B/blob/main/modeling_qwen.py#L522-L525
https://huggingface.co/rinna/nekomata-14b/blob/main/modeling_qwen.py#L522-L527
This discrepancy may be impacting the use of nekomata-14b with the latest https://github.com/QwenLM/Qwen repository's LoRA fine-tune implementation in a PyTorch 2 environment.
When attempting this, I encountered a
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation.
Hi @shoey-ucci , thank you for pointing it out.
I have just synced the modeling code with the latest official code.