Instructions to use MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp with Transformers:

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

pipe = pipeline("text-generation", model="MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp")
model = AutoModelForCausalLM.from_pretrained("MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp

SGLang

How to use MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp 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 "MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp" \
    --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": "MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp",
		"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 "MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp" \
        --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": "MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp with Docker Model Runner:
```
docker model run hf.co/MohammadOthman/Mistral-NeuralHermes-Merge-7B-slerp
```

Mistral-NeuralHermes-Merge-7B-slerp

Model Description

The Mistral-Merge-7B-slerp is a merged model which leverages the spherical linear interpolation (SLERP) technique to blend layers from two distinct transformer-based models. This merging strategy is aimed at synthesizing a model that incorporates the robust linguistic capabilities of OpenPipe/mistral-ft-optimized-1218 and the nuanced understanding of mlabonne/NeuralHermes-2.5-Mistral-7B.

Configuration

The merging process was configured to apply a SLERP method across all comparable layers of the two source models. Below is the YAML configuration used for merging:

slices:
  - sources:
      - model: OpenPipe/mistral-ft-optimized-1218
        layer_range: [0, 32]
      - model: mlabonne/NeuralHermes-2.5-Mistral-7B
        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

This configuration ensures that both self-attention and MLP (multi-layer perceptron) layers undergo interpolation with a gradient of weights to optimize the integration of features from both models.

Downloads last month: 18

Safetensors

Model size

7B params

Tensor type

BF16