Image-Text-to-Text
Transformers
Safetensors
interns1_pro
text-generation
conversational
custom_code
fp8
Instructions to use internlm/Intern-S1-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/Intern-S1-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="internlm/Intern-S1-Pro", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("internlm/Intern-S1-Pro", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use internlm/Intern-S1-Pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "internlm/Intern-S1-Pro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/Intern-S1-Pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/internlm/Intern-S1-Pro
- SGLang
How to use internlm/Intern-S1-Pro 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 "internlm/Intern-S1-Pro" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/Intern-S1-Pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "internlm/Intern-S1-Pro" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/Intern-S1-Pro", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use internlm/Intern-S1-Pro with Docker Model Runner:
docker model run hf.co/internlm/Intern-S1-Pro
Adapt tokenization_interns1.py to transformers>=5.0.0 (#15)
Browse files- Adapt tokenization_interns1.py to transformers>=5.0.0 (65efc111527b17792b13f09fbe7ddbb9d9df263e)
- Update: fix bug (7260346cf2e4d0d8faf80b45ff37a06a14e4b74a)
- Update: fix bug (7b33977d07711c291f31851e21f44f995dc74c3f)
Co-authored-by: Kevin Zhang <Zhangyc02@users.noreply.huggingface.co>
- tokenization_interns1.py +8 -6
tokenization_interns1.py
CHANGED
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@@ -24,11 +24,14 @@ from functools import lru_cache
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import regex as re
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import sentencepiece as spm
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from transformers.tokenization_utils_base import AddedToken, TextInput
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from transformers.tokenization_utils import PreTrainedTokenizer
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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@@ -506,6 +509,7 @@ class InternS1Tokenizer(PreTrainedTokenizer):
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pad_token="<|endoftext|>",
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clean_up_tokenization_spaces=False,
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split_special_tokens=False,
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**kwargs,
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):
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bos_token = (
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pad_token=pad_token,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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split_special_tokens=split_special_tokens,
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**kwargs,
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)
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text, kwargs = self.prepare_for_tokenization(text, **kwargs)
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if kwargs:
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logger.warning(f"Keyword arguments {kwargs} not recognized.")
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if hasattr(self, "do_lower_case") and self.do_lower_case:
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# convert non-special tokens to lowercase. Might be super slow as well?
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escaped_special_toks = [re.escape(s_tok) for s_tok in (self.all_special_tokens)]
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import regex as re
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import sentencepiece as spm
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import transformers
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from transformers.tokenization_utils_base import AddedToken, TextInput
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from transformers.utils import logging
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from packaging import version
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if version.parse(transformers.__version__) >= version.parse("5.0.0"):
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from transformers.tokenization_python import PreTrainedTokenizer
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else:
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from transformers.tokenization_utils import PreTrainedTokenizer
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logger = logging.get_logger(__name__)
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pad_token="<|endoftext|>",
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clean_up_tokenization_spaces=False,
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split_special_tokens=False,
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special_tokens_pattern="none",
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**kwargs,
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):
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bos_token = (
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pad_token=pad_token,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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split_special_tokens=split_special_tokens,
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special_tokens_pattern="none",
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**kwargs,
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)
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text, kwargs = self.prepare_for_tokenization(text, **kwargs)
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if hasattr(self, "do_lower_case") and self.do_lower_case:
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# convert non-special tokens to lowercase. Might be super slow as well?
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escaped_special_toks = [re.escape(s_tok) for s_tok in (self.all_special_tokens)]
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