Text Generation
Transformers
Safetensors
English
Japanese
llama
mergekit
Merge
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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("nitky/Swallow-70b-NVE-RP") model = AutoModelForMultimodalLM.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
| models: | |
| - model: tokyotech-llm/Swallow-70b-NVE-instruct-hf | |
| # no parameters necessary for base model | |
| - model: GOAT-AI/GOAT-70B-Storytelling # storytelling | |
| parameters: | |
| density: 1 | |
| weight: 0.25 | |
| - model: dreamgen/opus-v0.5-70b # creative roleplay | |
| parameters: | |
| density: 1 | |
| weight: 0.25 | |
| merge_method: dare_ties | |
| base_model: tokyotech-llm/Swallow-70b-NVE-instruct-hf | |
| dtype: bfloat16 | |
| name: Swallow-70b-NVE-RP-base | |
| models: | |
| - model: tokyotech-llm/Swallow-70b-NVE-instruct-hf | |
| # no parameters necessary for base model | |
| - model: Doctor-Shotgun/lzlv-limarpv3-l2-70b # roleplay configuration | |
| parameters: | |
| density: 1 | |
| weight: 0.25 | |
| merge_method: dare_ties | |
| base_model: tokyotech-llm/Swallow-70b-NVE-instruct-hf | |
| dtype: bfloat16 | |
| name: Swallow-70b-NVE-RP-flavor | |
| slices: | |
| - sources: | |
| - model: Swallow-70b-NVE-RP-base | |
| layer_range: [0, 80] | |
| - model: Swallow-70b-NVE-RP-flavor | |
| layer_range: [0, 80] | |
| merge_method: slerp | |
| base_model: Swallow-70b-NVE-RP-base | |
| parameters: | |
| t: | |
| - filter: self_attn | |
| value: [0, 0.5, 0.3, 0.7, 1] | |
| - filter: mlp | |
| value: [1, 0.5, 0.7, 0.3, 0] | |
| - value: 0.5 # fallback for rest of tensors | |
| dtype: bfloat16 | |
| name: Swallow-70b-NVE-RP | |