Text Generation
Transformers
PyTorch
mistral
conversational
text-generation-inference
4-bit precision
gptq
Instructions to use Shivam098/unstructured2structuredformat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shivam098/unstructured2structuredformat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Shivam098/unstructured2structuredformat") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Shivam098/unstructured2structuredformat") model = AutoModelForCausalLM.from_pretrained("Shivam098/unstructured2structuredformat") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Shivam098/unstructured2structuredformat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Shivam098/unstructured2structuredformat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Shivam098/unstructured2structuredformat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Shivam098/unstructured2structuredformat
- SGLang
How to use Shivam098/unstructured2structuredformat 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 "Shivam098/unstructured2structuredformat" \ --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": "Shivam098/unstructured2structuredformat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Shivam098/unstructured2structuredformat" \ --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": "Shivam098/unstructured2structuredformat", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Shivam098/unstructured2structuredformat with Docker Model Runner:
docker model run hf.co/Shivam098/unstructured2structuredformat
Upload tokenizer
Browse files- added_tokens.json +5 -0
- special_tokens_map.json +11 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +46 -0
added_tokens.json
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{
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"</s>": 2,
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"<s>": 1,
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"<unk>": 0
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}
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<unk>",
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"<s>",
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"</s>"
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],
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"bos_token": "<s>",
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"eos_token": "</s>",
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"pad_token": "</s>",
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"unk_token": "<unk>"
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}
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tokenizer.json
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
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size 493443
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"additional_special_tokens": [
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"<unk>",
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"<s>",
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"</s>"
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],
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"bos_token": "<s>",
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"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"legacy": true,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "</s>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"truncation_side": "left",
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"unk_token": "<unk>",
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"use_default_system_prompt": true
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}
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