Text Generation
Transformers
Safetensors
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llama
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text-generation-inference
Instructions to use nitky/Swallow-70b-NVE-RP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nitky/Swallow-70b-NVE-RP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nitky/Swallow-70b-NVE-RP")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nitky/Swallow-70b-NVE-RP") model = AutoModelForCausalLM.from_pretrained("nitky/Swallow-70b-NVE-RP") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nitky/Swallow-70b-NVE-RP with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nitky/Swallow-70b-NVE-RP" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nitky/Swallow-70b-NVE-RP", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nitky/Swallow-70b-NVE-RP
- SGLang
How to use nitky/Swallow-70b-NVE-RP 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 "nitky/Swallow-70b-NVE-RP" \ --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": "nitky/Swallow-70b-NVE-RP", "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 "nitky/Swallow-70b-NVE-RP" \ --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": "nitky/Swallow-70b-NVE-RP", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nitky/Swallow-70b-NVE-RP with Docker Model Runner:
docker model run hf.co/nitky/Swallow-70b-NVE-RP
GGUF version would be appciated
#1
by nonetrix - opened
GGUF version would be great, also GPTQ and AWQ as well
Actually, it's easy to create a gguf model yourself.
It works:
# if the LLM model path = "~/text-generation-webui/models"
docker pull ghcr.io/ggerganov/llama.cpp:full
docker run -v ~/text-generation-webui/models:/models ghcr.io/ggerganov/llama.cpp:full --convert /models/nitky_Swallow-70b-NVE-RP
docker run -v ~/text-generation-webui/models:/models ghcr.io/ggerganov/llama.cpp:full --quantize /models/nitky_Swallow-70b-NVE-RP/ggml-model-f16.gguf /models/nitky_Swallow-70b-NVE-RP-Q4_K_M.gguf Q4_K_M
I'll upload it if I have time.