Instructions to use TomGrc/FusionNet_34Bx2_MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TomGrc/FusionNet_34Bx2_MoE with Transformers:

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

pipe = pipeline("text-generation", model="TomGrc/FusionNet_34Bx2_MoE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TomGrc/FusionNet_34Bx2_MoE")
model = AutoModelForCausalLM.from_pretrained("TomGrc/FusionNet_34Bx2_MoE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TomGrc/FusionNet_34Bx2_MoE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TomGrc/FusionNet_34Bx2_MoE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TomGrc/FusionNet_34Bx2_MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TomGrc/FusionNet_34Bx2_MoE

SGLang

How to use TomGrc/FusionNet_34Bx2_MoE 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_34Bx2_MoE" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TomGrc/FusionNet_34Bx2_MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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_34Bx2_MoE" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TomGrc/FusionNet_34Bx2_MoE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TomGrc/FusionNet_34Bx2_MoE with Docker Model Runner:
```
docker model run hf.co/TomGrc/FusionNet_34Bx2_MoE
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

FusionNet_34Bx2_MoE

Fine-tuned model on English language using MoE method.

Model description

The FusionNet_34Bx2_MoE is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The FusionNet_34Bx2_MoE has 60.8B parameters, and this model is fine-tuned. Enjoy!

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("TomGrc/FusionNet_34Bx2_MoE")
model = AutoModelForCausalLM.from_pretrained("TomGrc/FusionNet_34Bx2_MoE")

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	77.07
AI2 Reasoning Challenge (25-Shot)	72.95
HellaSwag (10-Shot)	86.22
MMLU (5-Shot)	77.05
TruthfulQA (0-shot)	71.31
Winogrande (5-shot)	83.98
GSM8k (5-shot)	70.89

Downloads last month: 211

Safetensors

Model size

61B params

Tensor type

BF16

Model tree for TomGrc/FusionNet_34Bx2_MoE

Quantizations

6 models

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

86.220
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

77.050
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

71.310
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

83.980
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

70.890