Instructions to use oceansweep/mera-mix-4x7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oceansweep/mera-mix-4x7B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="oceansweep/mera-mix-4x7B-GGUF")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("oceansweep/mera-mix-4x7B-GGUF")
model = AutoModelForCausalLM.from_pretrained("oceansweep/mera-mix-4x7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use oceansweep/mera-mix-4x7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oceansweep/mera-mix-4x7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oceansweep/mera-mix-4x7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/oceansweep/mera-mix-4x7B-GGUF

SGLang

How to use oceansweep/mera-mix-4x7B-GGUF 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 "oceansweep/mera-mix-4x7B-GGUF" \
    --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": "oceansweep/mera-mix-4x7B-GGUF",
		"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 "oceansweep/mera-mix-4x7B-GGUF" \
        --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": "oceansweep/mera-mix-4x7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use oceansweep/mera-mix-4x7B-GGUF with Docker Model Runner:
```
docker model run hf.co/oceansweep/mera-mix-4x7B-GGUF
```

New: mera-mix-4x7B GGUF

This is a repo for GGUF quants of mera-mix-4x7B. Currently it holds the FP16 and Q8_0 items only.

Original: 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 76.37 on the openLLM eval v/s 72.7 by Mixtral-8x7B (as shown here).

You can try the model with the Mera Mixture Chat.

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

Downloads last month: 9

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

72.950
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

89.170
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.440
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

77.170
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

85.640
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

66.110