Instructions to use Dans-DiscountModels/Dans-StructureEvaluator-Small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dans-DiscountModels/Dans-StructureEvaluator-Small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Dans-DiscountModels/Dans-StructureEvaluator-Small")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Dans-DiscountModels/Dans-StructureEvaluator-Small") model = AutoModelForCausalLM.from_pretrained("Dans-DiscountModels/Dans-StructureEvaluator-Small") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Dans-DiscountModels/Dans-StructureEvaluator-Small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Dans-DiscountModels/Dans-StructureEvaluator-Small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dans-DiscountModels/Dans-StructureEvaluator-Small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Dans-DiscountModels/Dans-StructureEvaluator-Small
- SGLang
How to use Dans-DiscountModels/Dans-StructureEvaluator-Small 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 "Dans-DiscountModels/Dans-StructureEvaluator-Small" \ --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": "Dans-DiscountModels/Dans-StructureEvaluator-Small", "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 "Dans-DiscountModels/Dans-StructureEvaluator-Small" \ --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": "Dans-DiscountModels/Dans-StructureEvaluator-Small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Dans-DiscountModels/Dans-StructureEvaluator-Small with Docker Model Runner:
docker model run hf.co/Dans-DiscountModels/Dans-StructureEvaluator-Small
Upload 6 files
Browse files- config.json +2 -2
- generation_config.json +1 -1
- pytorch_model.bin +1 -1
config.json
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"_name_or_path": "Dans-DiscountModels/TinyMistral-v2-
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"architectures": [
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"sliding_window": 32,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.37.0
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"vocab_size": 32005
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"architectures": [
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"MistralForCausalLM"
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"sliding_window": 32,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.37.0",
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"use_cache": false,
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"vocab_size": 32005
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generation_config.json
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"_from_model_config": true,
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"transformers_version": "4.37.0
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"transformers_version": "4.37.0"
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pytorch_model.bin
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