Instructions to use meraGPT/mera-mix-4x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meraGPT/mera-mix-4x7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="meraGPT/mera-mix-4x7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("meraGPT/mera-mix-4x7B") model = AutoModelForCausalLM.from_pretrained("meraGPT/mera-mix-4x7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use meraGPT/mera-mix-4x7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "meraGPT/mera-mix-4x7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "meraGPT/mera-mix-4x7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/meraGPT/mera-mix-4x7B
- SGLang
How to use meraGPT/mera-mix-4x7B 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 "meraGPT/mera-mix-4x7B" \ --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": "meraGPT/mera-mix-4x7B", "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 "meraGPT/mera-mix-4x7B" \ --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": "meraGPT/mera-mix-4x7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use meraGPT/mera-mix-4x7B with Docker Model Runner:
docker model run hf.co/meraGPT/mera-mix-4x7B
Model mera-mix-4x7B
This is a mixture of experts (MoE) model that is half as large (4 experts instead of 8) as the Mixtral-8x7B while been comparable to it across different benchmarks. You can use it as a drop in replacement for your Mixtral-8x7B and get much faster inference.
mera-mix-4x7B achieves the score of 75.91 on the OpenLLM Eval and compares well with 72.7 by Mixtral-8x7B and 74.46 by Mixtral-8x22B.
You can try the model with the Mera Mixture Chat.
In addition, to the official Open LLM Leaderboard, the results on OpenLLM Eval have been validated by others as well (76.59).
Our own initial eval is available here (76.37).
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 75.91 |
| AI2 Reasoning Challenge (25-Shot) | 72.95 |
| HellaSwag (10-Shot) | 89.17 |
| MMLU (5-Shot) | 64.44 |
| TruthfulQA (0-shot) | 77.17 |
| Winogrande (5-shot) | 85.64 |
| GSM8k (5-shot) | 66.11 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard72.950
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard89.170
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.440
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard77.170
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard85.640
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard66.110