Instructions to use EmbeddedLLM/Mistral-7B-Merge-14-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0 with Transformers:

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

pipe = pipeline("text-generation", model="EmbeddedLLM/Mistral-7B-Merge-14-v0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EmbeddedLLM/Mistral-7B-Merge-14-v0")
model = AutoModelForCausalLM.from_pretrained("EmbeddedLLM/Mistral-7B-Merge-14-v0")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EmbeddedLLM/Mistral-7B-Merge-14-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EmbeddedLLM/Mistral-7B-Merge-14-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EmbeddedLLM/Mistral-7B-Merge-14-v0

SGLang

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0 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 "EmbeddedLLM/Mistral-7B-Merge-14-v0" \
    --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": "EmbeddedLLM/Mistral-7B-Merge-14-v0",
		"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 "EmbeddedLLM/Mistral-7B-Merge-14-v0" \
        --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": "EmbeddedLLM/Mistral-7B-Merge-14-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EmbeddedLLM/Mistral-7B-Merge-14-v0 with Docker Model Runner:
```
docker model run hf.co/EmbeddedLLM/Mistral-7B-Merge-14-v0
```

Update 2023-12-19

In light of dataset contamination issue among the merged models raised by the community in recent days, in particular berkeley-nest/Starling-LM-7B-alpha, and Q-bert/MetaMath-Cybertron-Starling, we decided to remake another model without the models mentioned. Additionally, their CC-by-NC-4.0 license is restrictive and thus are not suitable for an open model.

Model Description

This is an experiment to test merging 14 models using DARE TIES 🦙

The result is a base model that performs quite well but requires some further instruction fine-tuning.

The 14 models are as follows:

base model: mistralai/Mistral-7B-v0.1

The yaml config file for this model is here:

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: ehartford/dolphin-2.2.1-mistral-7b
    parameters:
      weight: 0.08
      density: 0.4
  - model: SciPhi/SciPhi-Mistral-7B-32k
    parameters:
      weight: 0.08
      density: 0.4
  - model: ehartford/samantha-1.2-mistral-7b
    parameters:
      weight: 0.08
      density: 0.4
  - model: Arc53/docsgpt-7b-mistral
    parameters:
      weight: 0.08
      density: 0.4
  - model: berkeley-nest/Starling-LM-7B-alpha
    parameters:
      weight: 0.08
      density: 0.4
  - model: Q-bert/MetaMath-Cybertron-Starling
    parameters:
      weight: 0.08
      density: 0.4
  - model: Open-Orca/Mistral-7B-OpenOrca
    parameters:
      weight: 0.08
      density: 0.4
  - model: v1olet/v1olet_marcoroni-go-bruins-merge-7B
    parameters:
      weight: 0.08
      density: 0.4
  - model: beowolx/MistralHermes-CodePro-7B-v1
    parameters:
      weight: 0.08
      density: 0.4
  - model: TIGER-Lab/MAmmoTH-7B-Mistral
    parameters:
      weight: 0.08
      density: 0.4
  - model: teknium/OpenHermes-2.5-Mistral-7B
    parameters:
      weight: 0.08
      density: 0.4
  - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
    parameters:
      weight: 0.08
      density: 0.4
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      weight: 0.08
      density: 0.4
  - model: mistralai/Mistral-7B-Instruct-v0.2
    parameters:
      weight: 0.08
      density: 0.5
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16