Instructions to use abacusai/Slerp-CM-mist-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abacusai/Slerp-CM-mist-dpo with Transformers:

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

pipe = pipeline("text-generation", model="abacusai/Slerp-CM-mist-dpo")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abacusai/Slerp-CM-mist-dpo")
model = AutoModelForCausalLM.from_pretrained("abacusai/Slerp-CM-mist-dpo")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use abacusai/Slerp-CM-mist-dpo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/abacusai/Slerp-CM-mist-dpo

SGLang

How to use abacusai/Slerp-CM-mist-dpo 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 "abacusai/Slerp-CM-mist-dpo" \
    --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": "abacusai/Slerp-CM-mist-dpo",
		"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 "abacusai/Slerp-CM-mist-dpo" \
        --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": "abacusai/Slerp-CM-mist-dpo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abacusai/Slerp-CM-mist-dpo with Docker Model Runner:
```
docker model run hf.co/abacusai/Slerp-CM-mist-dpo
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is a Slerp Merge of cookinai/CatMacaroni-Slerp and mncai/mistral-7b-dpo-v5.

Evaluation Results

HuggingFace Leaderboard

Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
73.1	69.62	87.09	64.81	62.82	81.45	72.78

The model did achieve an improvement in TruthfulQA over cookinai/CatMacaroni-Slerp and GSM8K over mncai/mistral-7b-dpo-v5 which was the goal of the merge leading to an average score that was a better than both. It is unclear why the TruthfulQA metric is still meaningfully lower than the base mncai/mistral-7b-dpo-v5.

Training Details

.yaml file for mergekit

slices:
  - sources:
      - model: cookinai/CatMacaroni-Slerp
        layer_range: [0, 32]
      - model: mncai/mistral-7b-dpo-v5
        layer_range: [0, 32]
merge_method: slerp
base_model: mncai/mistral-7b-dpo-v5
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 # fallback for rest of tensors
dtype: float16

Bias, Risks, and Limitations

The model has not been evaluated for safety and is only intended for research and experiments.

Downloads last month: 8,376

Safetensors

Model size

7B params

Tensor type

F16

Model tree for abacusai/Slerp-CM-mist-dpo

Merges

10 models

Quantizations

1 model

abacusai
/

Slerp-CM-mist-dpo

Evaluation Results

HuggingFace Leaderboard

Training Details

Bias, Risks, and Limitations

Model tree for abacusai/Slerp-CM-mist-dpo

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