Instructions to use EmbeddedLLM/Mistral-7B-Merge-14-v0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.5 with Transformers:

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

pipe = pipeline("text-generation", model="EmbeddedLLM/Mistral-7B-Merge-14-v0.5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EmbeddedLLM/Mistral-7B-Merge-14-v0.5")
model = AutoModelForCausalLM.from_pretrained("EmbeddedLLM/Mistral-7B-Merge-14-v0.5", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.5 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-v0.5"
# 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-v0.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.5

SGLang

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.5 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-v0.5" \
    --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-v0.5",
		"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-v0.5" \
        --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-v0.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.5 with Docker Model Runner:
```
docker model run hf.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Update 2024-01-21

Due to mlabonne/NeuralMarcoro14-7B updating its license to CC-BY-NC, our license will follow suit.

Model Description

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

We first merge 14 models to produce EmbeddedLLM/Mistral-7B-Merge-14-v0.3.
The model is merged again using DARE TIES with:

Open LLM Leaderboard

Average	71.96
ARC	68.69
HellaSwag	86.45
MMLU	65.65
TruthfulQA	59.12
Winogrande	80.66
GSM8K	71.19

Chat Template

Either ChatML or Llama-2 chat template.

Merge Configuration

The merge config file for this model is here:

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.3
    parameters:
      weight: 0.3
      density: 0.5
  - model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp
    parameters:
      weight: 0.2
      density: 0.5
  - model: openchat/openchat-3.5-0106
    parameters:
      weight: 0.2
      density: 0.5
  - model: mlabonne/NeuralMarcoro14-7B
    parameters:
      weight: 0.3
      density: 0.5
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1

parameters:
  int8_mask: true
tokenizer_source: union
dtype: bfloat16

Downloads last month: 46

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for EmbeddedLLM/Mistral-7B-Merge-14-v0.5

EmbeddedLLM/Mistral-7B-Merge-14-v0.3

Weyaxi/OpenHermes-2.5-neural-chat-v3-3-openchat-3.5-1210-Slerp

mlabonne/NeuralMarcoro14-7B

openchat/openchat-3.5-0106

Merge model

this model

Merges

1 model

Quantizations

1 model