Text Generation
Transformers
PyTorch
PEFT
English
llama
decapoda-research-7b-hf
prompt answering
text-generation-inference
8-bit precision
Instructions to use Sandiago21/llama-7b-hf-prompt-answering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sandiago21/llama-7b-hf-prompt-answering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sandiago21/llama-7b-hf-prompt-answering")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Sandiago21/llama-7b-hf-prompt-answering") model = AutoModelForCausalLM.from_pretrained("Sandiago21/llama-7b-hf-prompt-answering", device_map="auto") - PEFT
How to use Sandiago21/llama-7b-hf-prompt-answering with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Sandiago21/llama-7b-hf-prompt-answering with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sandiago21/llama-7b-hf-prompt-answering" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sandiago21/llama-7b-hf-prompt-answering", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Sandiago21/llama-7b-hf-prompt-answering
- SGLang
How to use Sandiago21/llama-7b-hf-prompt-answering 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 "Sandiago21/llama-7b-hf-prompt-answering" \ --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": "Sandiago21/llama-7b-hf-prompt-answering", "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 "Sandiago21/llama-7b-hf-prompt-answering" \ --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": "Sandiago21/llama-7b-hf-prompt-answering", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Sandiago21/llama-7b-hf-prompt-answering with Docker Model Runner:
docker model run hf.co/Sandiago21/llama-7b-hf-prompt-answering
Commit ·
0feb34f
1
Parent(s): f9bf1c4
add decapoda-research 7b hf bin files
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- .gitattributes +36 -0
- decapoda-research/llama-7b-hf/{pytorch_model-00001-of-00039.bin → pytorch_model-00001-of-00033.bin} +2 -2
- decapoda-research/llama-7b-hf/{pytorch_model-00002-of-00039.bin → pytorch_model-00002-of-00033.bin} +2 -2
- decapoda-research/llama-7b-hf/{pytorch_model-00003-of-00039.bin → pytorch_model-00003-of-00033.bin} +2 -2
- decapoda-research/llama-7b-hf/{pytorch_model-00004-of-00039.bin → pytorch_model-00004-of-00033.bin} +2 -2
- decapoda-research/llama-7b-hf/pytorch_model-00005-of-00033.bin +3 -0
- decapoda-research/llama-7b-hf/pytorch_model-00005-of-00039.bin +0 -3
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