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

Libraries

How to use arcee-ai/Saul-Instruct-Clown-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="arcee-ai/Saul-Instruct-Clown-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Saul-Instruct-Clown-7b")
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Saul-Instruct-Clown-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use arcee-ai/Saul-Instruct-Clown-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcee-ai/Saul-Instruct-Clown-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Saul-Instruct-Clown-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/arcee-ai/Saul-Instruct-Clown-7b

SGLang

How to use arcee-ai/Saul-Instruct-Clown-7b 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 "arcee-ai/Saul-Instruct-Clown-7b" \
    --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": "arcee-ai/Saul-Instruct-Clown-7b",
		"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 "arcee-ai/Saul-Instruct-Clown-7b" \
        --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": "arcee-ai/Saul-Instruct-Clown-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use arcee-ai/Saul-Instruct-Clown-7b with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Saul-Instruct-Clown-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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: 90

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for arcee-ai/Saul-Instruct-Clown-7b

Quantizations

1 model