Instructions to use saishf/Nous-Lotus-10.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use saishf/Nous-Lotus-10.7B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use saishf/Nous-Lotus-10.7B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "saishf/Nous-Lotus-10.7B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "saishf/Nous-Lotus-10.7B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M
- Ollama
How to use saishf/Nous-Lotus-10.7B-GGUF with Ollama:
ollama run hf.co/saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use saishf/Nous-Lotus-10.7B-GGUF with Docker Model Runner:
docker model run hf.co/saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M
- Lemonade
How to use saishf/Nous-Lotus-10.7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saishf/Nous-Lotus-10.7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Nous-Lotus-10.7B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
These are GGUF quants for https://huggingface.co/saishf/Nous-Lotus-10.7B
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
This model is a slerp between SnowLotus-v2 & Nous-Hermes-2-SOLAR, I found snowlotus was awesome to talk to but lacked when prompting with out-there characters. Nous Hermes seemed to handle those characters a lot better, so i decided to merge the two.
This is my first merge so it could perform badly or may not even work
Extra Info
Both models are solar based so context should be 4096
SnowLotus uses Alpaca
Nous Hermes uses ChatML
Both seem to work but i don't exactly know which performs better
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: BlueNipples/SnowLotus-v2-10.7B
layer_range: [0, 48]
- model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
layer_range: [0, 48]
merge_method: slerp
base_model: BlueNipples/SnowLotus-v2-10.7B
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
dtype: bfloat16
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