Instructions to use koesn/MonarchLake-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use koesn/MonarchLake-7B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("koesn/MonarchLake-7B-GGUF", dtype="auto")

llama-cpp-python

How to use koesn/MonarchLake-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="koesn/MonarchLake-7B-GGUF",
	filename="monarchlake-7b.IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use koesn/MonarchLake-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 koesn/MonarchLake-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf koesn/MonarchLake-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 koesn/MonarchLake-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf koesn/MonarchLake-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 koesn/MonarchLake-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf koesn/MonarchLake-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 koesn/MonarchLake-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf koesn/MonarchLake-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/koesn/MonarchLake-7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use koesn/MonarchLake-7B-GGUF with Ollama:
```
ollama run hf.co/koesn/MonarchLake-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use koesn/MonarchLake-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 koesn/MonarchLake-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 koesn/MonarchLake-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 koesn/MonarchLake-7B-GGUF to start chatting

Docker Model Runner
How to use koesn/MonarchLake-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/koesn/MonarchLake-7B-GGUF:Q4_K_M
```

Lemonade

How to use koesn/MonarchLake-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull koesn/MonarchLake-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MonarchLake-7B-GGUF-Q4_K_M

List all available models

lemonade list

MonarchLake-7B

Description

This repo contains GGUF format model files for MonarchLake-7B.

Files Provided

Name	Quant	Bits	File Size	Remark
monarchlake-7b.IQ3_XXS.gguf	IQ3_XXS	3	3.02 GB	3.06 bpw quantization
monarchlake-7b.IQ3_S.gguf	IQ3_S	3	3.18 GB	3.44 bpw quantization
monarchlake-7b.IQ3_M.gguf	IQ3_M	3	3.28 GB	3.66 bpw quantization mix
monarchlake-7b.Q4_0.gguf	Q4_0	4	4.11 GB	3.56G, +0.2166 ppl
monarchlake-7b.IQ4_NL.gguf	IQ4_NL	4	4.16 GB	4.25 bpw non-linear quantization
monarchlake-7b.Q4_K_M.gguf	Q4_K_M	4	4.37 GB	3.80G, +0.0532 ppl
monarchlake-7b.Q5_K_M.gguf	Q5_K_M	5	5.13 GB	4.45G, +0.0122 ppl
monarchlake-7b.Q6_K.gguf	Q6_K	6	5.94 GB	5.15G, +0.0008 ppl
monarchlake-7b.Q8_0.gguf	Q8_0	8	7.70 GB	6.70G, +0.0004 ppl

Parameters

path	type	architecture	rope_theta	sliding_win	max_pos_embed
macadeliccc/MonarchLake-7B	mistral	MistralForCausalLM	10000.0	4096	32768

Benchmarks

Original Model Card

MonarchLake-7B

This model equips AlphaMonarch-7B with a strong base of emotional intelligence.

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: mlabonne/AlphaMonarch-7B
        layer_range: [0, 32]
      - model: macadeliccc/WestLake-7b-v2-laser-truthy-dpo
        layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/AlphaMonarch-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: 44

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Model tree for koesn/MonarchLake-7B-GGUF

Base model

mlabonne/Monarch-7B

Finetuned

mlabonne/NeuralMonarch-7B

Finetuned

mlabonne/AlphaMonarch-7B

Quantized

(9)

this model