Instructions to use ntegrals/Mistral-Merge-Ties-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ntegrals/Mistral-Merge-Ties-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ntegrals/Mistral-Merge-Ties-Instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ntegrals/Mistral-Merge-Ties-Instruct")
model = AutoModelForCausalLM.from_pretrained("ntegrals/Mistral-Merge-Ties-Instruct")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ntegrals/Mistral-Merge-Ties-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ntegrals/Mistral-Merge-Ties-Instruct

SGLang

How to use ntegrals/Mistral-Merge-Ties-Instruct 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 "ntegrals/Mistral-Merge-Ties-Instruct" \
    --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": "ntegrals/Mistral-Merge-Ties-Instruct",
		"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 "ntegrals/Mistral-Merge-Ties-Instruct" \
        --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": "ntegrals/Mistral-Merge-Ties-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ntegrals/Mistral-Merge-Ties-Instruct with Docker Model Runner:
```
docker model run hf.co/ntegrals/Mistral-Merge-Ties-Instruct
```

Mistral-Merge-Ties-Instruct / README.md

ntegrals

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metadata

base_model:
  - ntegrals/Mistral-Merge-7B-Instruct-Slerp
  - OpenPipe/mistral-ft-optimized-1218
  - mistralai/Mistral-7B-v0.1
library_name: transformers
tags:
  - mergekit
  - merge

merged

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using mistralai/Mistral-7B-v0.1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: mistralai/Mistral-7B-v0.1
dtype: float16
merge_method: ties
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 32]
        model: mistralai/Mistral-7B-v0.1
      - layer_range: [0, 32]
        model: OpenPipe/mistral-ft-optimized-1218
        parameters:
          density: 0.5
          weight: 0.5
      - layer_range: [0, 32]
        model: ntegrals/Mistral-Merge-7B-Instruct-Slerp
        parameters:
          density: 0.5
          weight: 0.3
parameters:
  normalize: 1.0