Instructions to use gaochangkuan/model_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gaochangkuan/model_dir with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gaochangkuan/model_dir")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("gaochangkuan/model_dir") model = AutoModelForCausalLM.from_pretrained("gaochangkuan/model_dir") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use gaochangkuan/model_dir with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gaochangkuan/model_dir" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gaochangkuan/model_dir", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gaochangkuan/model_dir
- SGLang
How to use gaochangkuan/model_dir 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 "gaochangkuan/model_dir" \ --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": "gaochangkuan/model_dir", "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 "gaochangkuan/model_dir" \ --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": "gaochangkuan/model_dir", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use gaochangkuan/model_dir with Docker Model Runner:
docker model run hf.co/gaochangkuan/model_dir
Migrate model card from transformers-repo
Browse filesRead announcement at https://discuss.huggingface.co/t/announcement-all-model-cards-will-be-migrated-to-hf-co-model-repos/2755
Original file history: https://github.com/huggingface/transformers/commits/master/model_cards/gaochangkuan/model_dir/README.md
README.md
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| 1 |
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## Generating Chinese poetry by topic.
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```python
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from transformers import *
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tokenizer = BertTokenizer.from_pretrained("gaochangkuan/model_dir")
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model = AutoModelWithLMHead.from_pretrained("gaochangkuan/model_dir")
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prompt= '''<s>田园躬耕'''
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length= 84
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stop_token='</s>'
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temperature = 1.2
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repetition_penalty=1.3
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k= 30
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p= 0.95
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device ='cuda'
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seed=2020
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no_cuda=False
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prompt_text = prompt if prompt else input("Model prompt >>> ")
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encoded_prompt = tokenizer.encode(
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'<s>'+prompt_text+'<sep>',
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add_special_tokens=False,
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return_tensors="pt"
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)
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encoded_prompt = encoded_prompt.to(device)
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output_sequences = model.generate(
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input_ids=encoded_prompt,
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max_length=length,
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min_length=10,
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do_sample=True,
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early_stopping=True,
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num_beams=10,
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temperature=temperature,
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top_k=k,
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top_p=p,
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repetition_penalty=repetition_penalty,
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bad_words_ids=None,
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bos_token_id=tokenizer.bos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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length_penalty=1.2,
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no_repeat_ngram_size=2,
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num_return_sequences=1,
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attention_mask=None,
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decoder_start_token_id=tokenizer.bos_token_id,)
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generated_sequence = output_sequences[0].tolist()
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text = tokenizer.decode(generated_sequence)
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text = text[: text.find(stop_token) if stop_token else None]
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print(''.join(text).replace(' ','').replace('<pad>','').replace('<s>',''))
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```
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