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, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
Update README.md
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license: apache-2.0
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license: apache-2.0
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language:
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- zh
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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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).
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** 知未智能KDF
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- **Model type:** JIANG
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- **Language(s) (NLP):** Chinese
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- **License:** apache-2.0
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = 'kdf/jiang-base'
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model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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ret = model.generate(**tokenizer('今天天气不错', return_tensors='pt'), max_new_tokens=50, top_k=1)
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print(tokenizer.decode(ret[0]))
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```
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