Instructions to use svjack/gpt-dialogue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svjack/gpt-dialogue with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="svjack/gpt-dialogue")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("svjack/gpt-dialogue") model = AutoModelForCausalLM.from_pretrained("svjack/gpt-dialogue") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use svjack/gpt-dialogue with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "svjack/gpt-dialogue" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "svjack/gpt-dialogue", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/svjack/gpt-dialogue
- SGLang
How to use svjack/gpt-dialogue 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 "svjack/gpt-dialogue" \ --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": "svjack/gpt-dialogue", "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 "svjack/gpt-dialogue" \ --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": "svjack/gpt-dialogue", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use svjack/gpt-dialogue with Docker Model Runner:
docker model run hf.co/svjack/gpt-dialogue
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("svjack/gpt-dialogue")
model = AutoModelForCausalLM.from_pretrained("svjack/gpt-dialogue")
tokenizer.decode(
model.generate(
tokenizer.encode(
"你认为今天的天气怎么样?", return_tensors="pt", add_special_tokens=True
), max_length = 128,
num_beams=2,
top_p = 0.95,
top_k = 50,
repetition_penalty = 2.5,
length_penalty=1.0,
early_stopping=True,
)[0],
skip_special_tokens = True
)
'''
'你 认 为 今 天 的 天 气 怎 么 样 ?
我 认 为 这 会 很 冷 , 但 是 春 天 是 如 此 美 丽 。
您 最 喜 欢 哪 个 季 节 ?
我 真 的 不 知 道 。 也 许 在 山 上 有 一 些 雪 松 石 。
听 起 来 不 错 。 我 们 可 以 去 海 滩 吗 ?
当 然 。 我 们 应 该 尽 快 去 那 里 。
好 的 。 让 我 们 看 看 它 是 否 准 备 好 了 。
现 在 下 雨 了 。
我 们 要 去 哪 里 见 面 ?
我 们'
'''
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