Instructions to use mhenrichsen/context-aware-splitter-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mhenrichsen/context-aware-splitter-1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mhenrichsen/context-aware-splitter-1b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mhenrichsen/context-aware-splitter-1b") model = AutoModelForCausalLM.from_pretrained("mhenrichsen/context-aware-splitter-1b") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mhenrichsen/context-aware-splitter-1b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mhenrichsen/context-aware-splitter-1b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mhenrichsen/context-aware-splitter-1b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mhenrichsen/context-aware-splitter-1b
- SGLang
How to use mhenrichsen/context-aware-splitter-1b 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 "mhenrichsen/context-aware-splitter-1b" \ --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": "mhenrichsen/context-aware-splitter-1b", "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 "mhenrichsen/context-aware-splitter-1b" \ --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": "mhenrichsen/context-aware-splitter-1b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mhenrichsen/context-aware-splitter-1b with Docker Model Runner:
docker model run hf.co/mhenrichsen/context-aware-splitter-1b
Commit ·
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Parent(s): 25397c3
Update README.md
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README.md
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@@ -18,6 +18,51 @@ It returns a dict with the keys:
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- splits: list[str]
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- topic: str
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Example:
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```
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### Instruction:
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- splits: list[str]
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- topic: str
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## Code example
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```python
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from transformers import AutoTokenizer, TextStreamer, AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained("mhenrichsen/context-aware-splitter-1b")
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tokenizer = AutoTokenizer.from_pretrained("mhenrichsen/context-aware-splitter-1b")
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streamer = TextStreamer(tokenizer, skip_special_tokens=True)
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WORD_SPLIT_COUNT = 50
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prompt_template = """### Instruction:
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Din opgave er at segmentere en given tekst i separate dele, så hver del giver mening og kan læses uafhængigt af de andre. Hvis det giver mening, må der kan være et overlap mellem delene. Hver del skal ideelt indeholde {word_count} ord.
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### Input:
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{text}
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### Response:
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"""
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artikel = """Kina er stærkt utilfreds med, at Tysklands udenrigsminister, Annalena Baerbock, har omtalt den kinesiske præsident Xi Jinping som en diktator.
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- Bemærkningerne fra Tyskland er ekstremt absurde, krænker Kinas politiske værdighed alvorligt og er en åben politisk provokation, udtalte talsperson fra det kinesiske udenrigsministerium Mao Ning i går ifølge CNN.
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Bemærkningen fra udenrigsminister Annalena Baerbock faldt i et interview om krigen i Ukraine med Fox News i sidste uge.
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- Hvis Putin skulle vinde denne krig, hvilket signal ville det så sende til andre diktatorer i verden, som Xi, som den kinesiske præsident?, sagde hun.
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Tysklands ambassadør i Kina, Patricia Flor, har som konsekvens af udtalelsen været til en kammeratlig samtale, oplyser det tyske udenrigsministerium til CNN."""
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tokens = tokenizer(
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prompt_template.format(text=artikel, word_count=WORD_SPLIT_COUNT),
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return_tensors='pt'
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)['input_ids']
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# Generate output
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generation_output = model.generate(
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tokens,
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streamer=streamer,
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max_length = 8194,
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eos_token_id = 29913
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)
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```
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Example:
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```
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### Instruction:
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