Text Generation
Transformers
Safetensors
llama
financial
sentiment
pruning
text-generation-inference
Instructions to use oopere/Llama-FinSent-S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oopere/Llama-FinSent-S with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="oopere/Llama-FinSent-S")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("oopere/Llama-FinSent-S") model = AutoModelForCausalLM.from_pretrained("oopere/Llama-FinSent-S", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use oopere/Llama-FinSent-S with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "oopere/Llama-FinSent-S" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "oopere/Llama-FinSent-S", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/oopere/Llama-FinSent-S
- SGLang
How to use oopere/Llama-FinSent-S 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 "oopere/Llama-FinSent-S" \ --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": "oopere/Llama-FinSent-S", "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 "oopere/Llama-FinSent-S" \ --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": "oopere/Llama-FinSent-S", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use oopere/Llama-FinSent-S with Docker Model Runner:
docker model run hf.co/oopere/Llama-FinSent-S
Upload tokenizer
Browse files- tokenizer.json +2 -2
- tokenizer_config.json +8 -0
tokenizer.json
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tokenizer_config.json
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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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"bos_token": "<|begin_of_text|>",
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"rstrip": false,
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"single_word": false,
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"special": true
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"128256": {
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"content": "<|endofresponse|>",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false,
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"special": false
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}
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},
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"bos_token": "<|begin_of_text|>",
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