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---
license: llama3.1
base_model:
- meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- Text Generation
- llama3.1
- text-generation-inference
- Inference Endpoints
- Transformers
- Fusion
language:
- en
---
# Llama-3.1-8B-Fusion-8020
## Overview
`Llama-3.1-8B-Fusion-9010` is a mixed model that combines the strengths of two powerful Llama-based models: [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite) and [mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated](https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated). The weights are blended in a 9:1 ratio, with 90% of the weights from SuperNova-Lite and 10% from the abliterated Meta-Llama-3.1-8B-Instruct model.
**Although it's a simple mix, the model is usable, and no gibberish has appeared**.
This is an experiment. Later, I will test the 9:1(https://huggingface.co/huihui-ai/Llama-3.1-8B-Fusion-9010), 7:3, 6:4, and 5:5 ratios separately to see how much impact they have on the model.
## Model Details
- **Base Models:**
- [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite) (90%)
- [mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated](https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated) (10%)
- **Model Size:** 8B parameters
- **Architecture:** Llama 3.1
- **Mixing Ratio:** 9:1 (SuperNova-Lite:Meta-Llama-3.1-8B-Instruct-abliterated)
## Key Features
- **SuperNova-Lite Contributions (90%):** Llama-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai, based on the Llama-3.1-8B-Instruct architecture.
- **Meta-Llama-3.1-8B-Instruct-abliterated Contributions (10%):** This is an uncensored version of Llama 3.1 8B Instruct created with abliteration.
## Usage
You can use this mixed model in your applications by loading it with Hugging Face's `transformers` library:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import time
mixed_model_name = "huihui-ai/Llama-3.1-8B-Fusion-8020"
# Check if CUDA is available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load model and tokenizer
mixed_model = AutoModelForCausalLM.from_pretrained(mixed_model_name, device_map=device, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(mixed_model_name)
# Ensure the tokenizer has pad_token_id set
tokenizer.pad_token_id = tokenizer.eos_token_id
# Input loop
print("Start inputting text for inference (type 'exit' to quit)")
while True:
prompt = input("Enter your prompt: ")
if prompt.lower() == "exit":
print("Exiting inference loop.")
break
# Inference phase: Generate text using the modified model
chat = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
# Prepare input data
input_ids = tokenizer.apply_chat_template(
chat, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(device)
# Use TextStreamer for streaming output
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
# Record the start time
start_time = time.time()
# Generate text and stream output character by character
outputs = mixed_model.generate(
input_ids,
max_new_tokens=8192,
do_sample=True,
temperature=0.6,
top_p=0.9,
streamer=streamer # Enable streaming output
)
# Record the end time
end_time = time.time()
# Calculate the number of generated tokens
generated_tokens = outputs[0][input_ids.shape[-1]:].shape[0]
# Calculate the total time taken
total_time = end_time - start_time
# Calculate tokens generated per second
tokens_per_second = generated_tokens / total_time
print(f"\nGenerated {generated_tokens} tokens in total, took {total_time:.2f} seconds, generating {tokens_per_second:.2f} tokens per second.")
```
## Evaluations
We will be submitting this model to the OpenLLM Leaderboard for a more conclusive benchmark - but here are our internal benchmarks using the main branch of lm evaluation harness: |