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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-7030
## Overview
`Llama-3.1-8B-Fusion-7030` 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 7:3 ratio, with 70% of the weights from SuperNova-Lite and 30% 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. I test the [9:1](https://huggingface.co/huihui-ai/Llama-3.1-8B-Fusion-9010), [8:2](https://huggingface.co/huihui-ai/Llama-3.1-8B-Fusion-8020), [7:3](https://huggingface.co/huihui-ai/Llama-3.1-8B-Fusion-7030), [6:4](https://huggingface.co/huihui-ai/Llama-3.1-8B-Fusion-6040) and [5:5](https://huggingface.co/huihui-ai/Llama-3.1-8B-Fusion-5050) ratios separately to see how much impact they have on the model.
All model evaluation reports will be provided subsequently.
## Model Details
- **Base Models:**
- [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite) (70%)
- [mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated](https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated) (30%)
- **Model Size:** 8B parameters
- **Architecture:** Llama 3.1
- **Mixing Ratio:** 7:3 (SuperNova-Lite:Meta-Llama-3.1-8B-Instruct-abliterated)
## Key Features
- **SuperNova-Lite Contributions (70%):** 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 (30%):** 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-7030"
# 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
The following data has been re-evaluated and calculated as the average for each test.
| Benchmark | SuperNova-Lite | Meta-Llama-3.1-8B-Instruct-abliterated | Llama-3.1-8B-Fusion-9010 | Llama-3.1-8B-Fusion-8020 | Llama-3.1-8B-Fusion-7030 | Llama-3.1-8B-Fusion-6040 | Llama-3.1-8B-Fusion-5050 |
|-------------|----------------|----------------------------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|
| IF_Eval | 82.09 | 76.29 | 82.44 | 82.93 | **83.10** | 82.94 | 82.03 |
| MMLU Pro | **35.87** | 33.1 | 35.65 | 35.32 | 34.91 | 34.5 | 33.96 |
| TruthfulQA | **64.35** | 53.25 | 62.67 | 61.04 | 59.09 | 57.8 | 56.75 |
| BBH | **49.48** | 44.87 | 48.86 | 48.47 | 48.30 | 48.19 | 47.93 |
| GPQA | 31.98 | 29.50 | 32.25 | 32.38 | **32.61** | 31.14 | 30.6 |
The script used for evaluation can be found inside this repository under /eval.sh, or click [here](https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated/blob/main/eval.sh)

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{
"_name_or_path": "huihui-ai/Llama-3.1-8B-Fusion-7030",
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"eos_token_id": [
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128008,
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"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pretraining_tp": 1,
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"torch_dtype": "bfloat16",
"transformers_version": "4.43.4",
"use_cache": true,
"vocab_size": 128256
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{"framework": "pytorch", "task": "others", "allow_remote": true}

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# Install required package
pip install antlr4-python3-runtime==4.11 immutabledict langdetect nltk lm_eval
python -c "import nltk; nltk.download('punkt')"
MODEL_PATHS=(
huihui-ai/Llama-3.1-8B-Fusion-7030
)
for MODEL_PATH in "${MODEL_PATHS[@]}"; do
MODEL_NAME=$(basename "$MODEL_PATH")
MODEL_DIR="./results/$MODEL_NAME"
mkdir -p "$MODEL_DIR"
MODEL_ARGS="trust_remote_code=True,pretrained=$MODEL_PATH,dtype=bfloat16"
BASE_COMMAND="accelerate launch -m lm_eval --model hf --model_args $MODEL_ARGS --batch_size 4 --fewshot_as_multiturn --apply_chat_template"
# IFEval
$BASE_COMMAND --tasks leaderboard_ifeval --fewshot_as_multiturn --output_path "$MODEL_DIR/ifeval"
# BBH (Big-Bench Hard)
$BASE_COMMAND --tasks leaderboard_bbh --num_fewshot 3 --fewshot_as_multiturn --output_path "$MODEL_DIR/bbh"
# GPQA
$BASE_COMMAND --tasks leaderboard_gpqa --fewshot_as_multiturn --output_path "$MODEL_DIR/gpqa"
# MMLU-Pro
$BASE_COMMAND --tasks leaderboard_mmlu_pro --num_fewshot 5 --fewshot_as_multiturn --output_path "$MODEL_DIR/mmlu_pro"
# TruthfulQA
$BASE_COMMAND --tasks truthfulqa_mc2 --fewshot_as_multiturn --output_path "$MODEL_DIR/truthfulqa"
done

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"_from_model_config": true,
"bos_token_id": 128000,
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