Instructions to use RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf",
	filename="Saul-Instruct-Clown-7b.IQ3_M.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 RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-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 RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-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 RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-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 RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-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 RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf:Q4_K_M
```

Unsloth Studio new

How to use RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-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 RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-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 RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-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 RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf to start chatting

Docker Model Runner
How to use RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/arcee-ai_-_Saul-Instruct-Clown-7b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.arcee-ai_-_Saul-Instruct-Clown-7b-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Saul-Instruct-Clown-7b - GGUF

Model creator: https://huggingface.co/arcee-ai/
Original model: https://huggingface.co/arcee-ai/Saul-Instruct-Clown-7b/

Name	Quant method	Size
Saul-Instruct-Clown-7b.Q2_K.gguf	Q2_K	2.53GB
Saul-Instruct-Clown-7b.IQ3_XS.gguf	IQ3_XS	2.81GB
Saul-Instruct-Clown-7b.IQ3_S.gguf	IQ3_S	2.96GB
Saul-Instruct-Clown-7b.Q3_K_S.gguf	Q3_K_S	2.95GB
Saul-Instruct-Clown-7b.IQ3_M.gguf	IQ3_M	3.06GB
Saul-Instruct-Clown-7b.Q3_K.gguf	Q3_K	3.28GB
Saul-Instruct-Clown-7b.Q3_K_M.gguf	Q3_K_M	3.28GB
Saul-Instruct-Clown-7b.Q3_K_L.gguf	Q3_K_L	3.56GB
Saul-Instruct-Clown-7b.IQ4_XS.gguf	IQ4_XS	3.67GB
Saul-Instruct-Clown-7b.Q4_0.gguf	Q4_0	3.83GB
Saul-Instruct-Clown-7b.IQ4_NL.gguf	IQ4_NL	3.87GB
Saul-Instruct-Clown-7b.Q4_K_S.gguf	Q4_K_S	3.86GB
Saul-Instruct-Clown-7b.Q4_K.gguf	Q4_K	4.07GB
Saul-Instruct-Clown-7b.Q4_K_M.gguf	Q4_K_M	4.07GB
Saul-Instruct-Clown-7b.Q4_1.gguf	Q4_1	4.24GB
Saul-Instruct-Clown-7b.Q5_0.gguf	Q5_0	4.65GB
Saul-Instruct-Clown-7b.Q5_K_S.gguf	Q5_K_S	4.65GB
Saul-Instruct-Clown-7b.Q5_K.gguf	Q5_K	4.78GB
Saul-Instruct-Clown-7b.Q5_K_M.gguf	Q5_K_M	4.78GB
Saul-Instruct-Clown-7b.Q5_1.gguf	Q5_1	5.07GB
Saul-Instruct-Clown-7b.Q6_K.gguf	Q6_K	5.53GB
Saul-Instruct-Clown-7b.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 tags: - merge - mergekit - CorticalStack/pastiche-crown-clown-7b-dare-dpo - Equall/Saul-Instruct-v1

Saul-Instruct-Clown-7b

Saul-Instruct-Clown-7b is a merge of the following models using mergekit:

🏆 Evaluation

OpenLLM

Saul-Instruct-Clown-7b OpenLLM benchmark suite

Model	Average	arc	HellaSwag	mmlu	TruthfulQA	gsm8k
arcee-ai/Saul-Instruct-Clown-7b	72.79	68.26	86.28	63.12	64.68	83.43

🧩 Configuration

  slices:
    - sources:
        - model: CorticalStack/pastiche-crown-clown-7b-dare-dpo
          layer_range: [0, 32]
        - model: Equall/Saul-Instruct-v1
          layer_range: [0, 32]
  merge_method: slerp
  base_model: CorticalStack/pastiche-crown-clown-7b-dare-dpo
  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: 362

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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