Instructions to use kdf/jiang-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kdf/jiang-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kdf/jiang-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("kdf/jiang-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kdf/jiang-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kdf/jiang-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kdf/jiang-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kdf/jiang-base
- SGLang
How to use kdf/jiang-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 "kdf/jiang-base" \ --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": "kdf/jiang-base", "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 "kdf/jiang-base" \ --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": "kdf/jiang-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kdf/jiang-base with Docker Model Runner:
docker model run hf.co/kdf/jiang-base
| license: apache-2.0 | |
| language: | |
| - zh | |
| # Model Card for Model ID | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). | |
| ## Model Details | |
| ### Model Description | |
| <!-- Provide a longer summary of what this model is. --> | |
| - **Developed by:** 知未智能KDF | |
| - **Model type:** JIANG | |
| - **Language(s) (NLP):** Chinese | |
| - **License:** apache-2.0 | |
| ## How to Get Started with the Model | |
| Use the code below to get started with the model. | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_id = 'kdf/jiang-base' | |
| model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True) | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) | |
| ret = model.generate(**tokenizer('今天天气不错', return_tensors='pt'), max_new_tokens=50, top_k=1) | |
| print(tokenizer.decode(ret[0])) | |
| ``` |