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")

# pip install -U transformers accelerate
# 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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

FusionNet_7Bx2_MoE_14B / README.md

TomGrc

Adding Evaluation Results (#10)

e4076fa verified over 2 years ago

preview code

Raw History Blame Contribute Delete

3.65 kB

metadata

language:
  - en
license: mit
tags:
  - moe
model-index:
  - name: FusionNet_7Bx2_MoE_14B
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 73.55
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 88.84
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 64.68
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 69.6
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 88.16
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 70.66
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_7Bx2_MoE_14B
          name: Open LLM Leaderboard

FusionNet

Fine-tuned model on English language using MoE method.

Model description

The FusionNet is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The FusionNet has 12.9B parameters, and this model is fine-tuned. Enjoy!

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	75.91
AI2 Reasoning Challenge (25-Shot)	73.55
HellaSwag (10-Shot)	88.84
MMLU (5-Shot)	64.68
TruthfulQA (0-shot)	69.60
Winogrande (5-shot)	88.16
GSM8k (5-shot)	70.66