Instructions to use Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base with Transformers:

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

pipe = pipeline("text-generation", model="Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base")
model = AutoModelForCausalLM.from_pretrained("Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base

SGLang

How to use Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base 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 "Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base" \
    --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": "Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base",
		"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 "Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base" \
        --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": "Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base with Docker Model Runner:
```
docker model run hf.co/Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-3.1-SuperNova-Lite_TIES_with_Base

This is a merge of pre-trained language models created using mergekit.

Merge Details/Method

This is a merge of arcee-ai/Llama-3.1-SuperNova-Lite with its base meta-llama/Llama-3.1-8B (the base model being: the model which the instruct model was fine-tuned on - even though in our case, arcee-ai/Llama-3.1-SuperNova-Lite, was fine-tuned, etc on top of meta-llama/Llama-3.1-8B-Instruct and not directly on top of meta-llama/Llama-3.1-8B)

This model was merged using the TIES merge method using meta-llama/Llama-3.1-8B as a base.

The merge was inspired by RomboDawg's (Replete-AI) TIES merge of Qwen/Qwen2.5-7B-Instruct with its base Qwen/Qwen2.5-7B, which topped the OpenLLM Learderboard for the highest Average score for a 7B parameter model.

After experimenting and discussing/researching the merge with Rombodawg, I looked into mergekit's TIES merge method some more, which led me to find a pertinent parameter that we weren't utilizing for our TIES merge: density. I decided to use density along with the weight parameter to see if we could restore some of the instruction following that our merges seemed to lack in comparison to the original Instruct model. The resulant merges turned out to be great! By using the density parameter along with the weight parameter, we were able to restore more of the Instruction following which was diminished and/or not present when solely using the weight parameter for our TIES merge.

The way this works is: the Instruct model is TIES merged with the base model, with the weight = 1 and density = 1. After the merge is complete, the merge's .json config files (excluding 'model.safetensors.index.json') are replaced with the original Instruct's .json config files.

Models Merged

The following models were included in the merge:

/Users/jsarnecki/opt/Workspace/arcee-ai/Llama-3.1-SuperNova-Lite

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: "/Users/jsarnecki/opt/Workspace/arcee-ai/Llama-3.1-SuperNova-Lite"
    parameters:
      weight: 1
      density: 1

  - model: "/Users/jsarnecki/opt/Workspace/arcee-ai/Llama-3.1-SuperNova-Lite"
    parameters:
      weight: 1
      density: 1

merge_method: ties
base_model: "/Users/jsarnecki/opt/Workspace/meta-llama/Llama-3.1-8B"
parameters:
  density: 1
  normalize: true
  int8_mask: true
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Average Score	43.07
IFEval (0-Shot)	80.96
BBH (3-Shot)	51.10
MATH Lvl 5 (4-Shot)	15.56
GPQA (0-shot)	30.96
MuSR (0-shot)	41.01
MMLU-PRO (5-shot)	38.80

Downloads last month: 20

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base

Merges

9 models

Quantizations

6 models

Space using Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base 1

Paper for Joseph717171/Llama-3.1-SuperNova-8B-Lite_TIES_with_Base

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19