Instructions to use TomGrc/FusionNet_7Bx2_MoE_14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TomGrc/FusionNet_7Bx2_MoE_14B with Transformers:

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

pipe = pipeline("text-generation", model="TomGrc/FusionNet_7Bx2_MoE_14B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TomGrc/FusionNet_7Bx2_MoE_14B")
model = AutoModelForCausalLM.from_pretrained("TomGrc/FusionNet_7Bx2_MoE_14B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TomGrc/FusionNet_7Bx2_MoE_14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TomGrc/FusionNet_7Bx2_MoE_14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TomGrc/FusionNet_7Bx2_MoE_14B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TomGrc/FusionNet_7Bx2_MoE_14B

SGLang

How to use TomGrc/FusionNet_7Bx2_MoE_14B 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 "TomGrc/FusionNet_7Bx2_MoE_14B" \
    --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": "TomGrc/FusionNet_7Bx2_MoE_14B",
		"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 "TomGrc/FusionNet_7Bx2_MoE_14B" \
        --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": "TomGrc/FusionNet_7Bx2_MoE_14B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TomGrc/FusionNet_7Bx2_MoE_14B with Docker Model Runner:
```
docker model run hf.co/TomGrc/FusionNet_7Bx2_MoE_14B
```

How do you have a score of 88 on winogrande?

by distantquant - opened Jan 20, 2024

Discussion

distantquant

Jan 20, 2024

For reference it is 2-4 points above all other top models, which makes no sense unless you had some insane new method of training. This is especially suspect for only a 7B MoE.

How was this trained?

Otherwise I will assume contamination.

TomGrc changed discussion status to closed Jan 20, 2024

testerav

Jan 20, 2024

Otherwise I will assume contamination.

i feel that too , their is no info on the model whtsoever and he closes the discussions which are problamatic.

TomGrc changed discussion status to open Jan 20, 2024

TomGrc

Owner Jan 20, 2024

If you assume this model is contaminated, please provide data contamination check results to confirm that. Otherwise, I don't have the responsibility to do a test due to the MIT license.

TomGrc changed discussion status to closed Jan 20, 2024

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