Instructions to use saishf/Nous-Lotus-10.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saishf/Nous-Lotus-10.7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="saishf/Nous-Lotus-10.7B-GGUF",
	filename="Nous-Lotus-10.7B-Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use saishf/Nous-Lotus-10.7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -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-server -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

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -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/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -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

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 Studio new

How to use saishf/Nous-Lotus-10.7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for saishf/Nous-Lotus-10.7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for saishf/Nous-Lotus-10.7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for saishf/Nous-Lotus-10.7B-GGUF to start chatting

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

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

Downloads last month: 34

GGUF

Model size

11B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for saishf/Nous-Lotus-10.7B-GGUF

BlueNipples/SnowLotus-v2-10.7B

NousResearch/Nous-Hermes-2-SOLAR-10.7B

Merge model

this model