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

Libraries

How to use TomGrc/FusionNet_linear with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TomGrc/FusionNet_linear")
model = AutoModelForCausalLM.from_pretrained("TomGrc/FusionNet_linear")
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_linear with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TomGrc/FusionNet_linear"
# 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_linear",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TomGrc/FusionNet_linear

SGLang

How to use TomGrc/FusionNet_linear 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_linear" \
    --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_linear",
		"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_linear" \
        --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_linear",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

FusionNet_linear

Fine-tuned model on English language using linear Fusion method.

Model description

This is an experiment with the linear Fusion method of FusionNet. This model has 10.7B parameters, and this model is fine-tuned. Enjoy!

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.43
AI2 Reasoning Challenge (25-Shot)	71.25
HellaSwag (10-Shot)	88.44
MMLU (5-Shot)	66.35
TruthfulQA (0-shot)	71.94
Winogrande (5-shot)	83.27
GSM8k (5-shot)	65.35

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Safetensors

Model size

11B params

Tensor type

F16

Model tree for TomGrc/FusionNet_linear

Quantizations

3 models

Evaluation results

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

71.250
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

88.440
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

66.350
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

71.940
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

83.270
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

65.350