Instructions to use scaledown/ScaleDown-7B-slerp-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use scaledown/ScaleDown-7B-slerp-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="scaledown/ScaleDown-7B-slerp-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("scaledown/ScaleDown-7B-slerp-v0.1")
model = AutoModelForCausalLM.from_pretrained("scaledown/ScaleDown-7B-slerp-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use scaledown/ScaleDown-7B-slerp-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "scaledown/ScaleDown-7B-slerp-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "scaledown/ScaleDown-7B-slerp-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/scaledown/ScaleDown-7B-slerp-v0.1

SGLang

How to use scaledown/ScaleDown-7B-slerp-v0.1 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 "scaledown/ScaleDown-7B-slerp-v0.1" \
    --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": "scaledown/ScaleDown-7B-slerp-v0.1",
		"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 "scaledown/ScaleDown-7B-slerp-v0.1" \
        --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": "scaledown/ScaleDown-7B-slerp-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use scaledown/ScaleDown-7B-slerp-v0.1 with Docker Model Runner:
```
docker model run hf.co/scaledown/ScaleDown-7B-slerp-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ScaleDown-7B-slerp-v0.1

This model is a merge of the following models made with mergekit:

🧩 Configuration

slices:
  - sources:
      - model: OpenPipe/mistral-ft-optimized-1218
        layer_range: [0, 32]
      - model: jondurbin/bagel-dpo-7b-v0.1
        layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	71.57
AI2 Reasoning Challenge (25-Shot)	68.00
HellaSwag (10-Shot)	85.70
MMLU (5-Shot)	65.26
TruthfulQA (0-shot)	61.90
Winogrande (5-shot)	81.37
GSM8k (5-shot)	67.17

Downloads last month: 225

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for scaledown/ScaleDown-7B-slerp-v0.1

Quantizations

3 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

68.000
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

85.700
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

65.260
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

61.900
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

81.370
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

67.170