118 lines
5.9 KiB
Markdown
118 lines
5.9 KiB
Markdown
---
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license: llama3.1
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base_model:
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- meta-llama/Meta-Llama-3.1-8B-Instruct
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tags:
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- Text Generation
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- llama3.1
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- text-generation-inference
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- Inference Endpoints
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- Transformers
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- Fusion
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language:
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- en
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---
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# Llama-3.1-8B-Fusion-7030
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## Overview
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`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.
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**Although it's a simple mix, the model is usable, and no gibberish has appeared**.
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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.
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All model evaluation reports will be provided subsequently.
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## Model Details
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- **Base Models:**
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- [arcee-ai/Llama-3.1-SuperNova-Lite](https://huggingface.co/arcee-ai/Llama-3.1-SuperNova-Lite) (70%)
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- [mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated](https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated) (30%)
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- **Model Size:** 8B parameters
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- **Architecture:** Llama 3.1
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- **Mixing Ratio:** 7:3 (SuperNova-Lite:Meta-Llama-3.1-8B-Instruct-abliterated)
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## Key Features
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- **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.
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- **Meta-Llama-3.1-8B-Instruct-abliterated Contributions (30%):** This is an uncensored version of Llama 3.1 8B Instruct created with abliteration.
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## Usage
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You can use this mixed model in your applications by loading it with Hugging Face's `transformers` library:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
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import time
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mixed_model_name = "huihui-ai/Llama-3.1-8B-Fusion-7030"
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# Check if CUDA is available
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# Load model and tokenizer
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mixed_model = AutoModelForCausalLM.from_pretrained(mixed_model_name, device_map=device, torch_dtype=torch.bfloat16)
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tokenizer = AutoTokenizer.from_pretrained(mixed_model_name)
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# Ensure the tokenizer has pad_token_id set
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tokenizer.pad_token_id = tokenizer.eos_token_id
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# Input loop
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print("Start inputting text for inference (type 'exit' to quit)")
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while True:
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prompt = input("Enter your prompt: ")
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if prompt.lower() == "exit":
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print("Exiting inference loop.")
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break
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# Inference phase: Generate text using the modified model
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chat = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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# Prepare input data
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input_ids = tokenizer.apply_chat_template(
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chat, tokenize=True, add_generation_prompt=True, return_tensors="pt"
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).to(device)
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# Use TextStreamer for streaming output
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streamer = TextStreamer(tokenizer, skip_special_tokens=True)
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# Record the start time
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start_time = time.time()
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# Generate text and stream output character by character
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outputs = mixed_model.generate(
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input_ids,
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max_new_tokens=8192,
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do_sample=True,
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temperature=0.6,
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top_p=0.9,
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streamer=streamer # Enable streaming output
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)
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# Record the end time
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end_time = time.time()
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# Calculate the number of generated tokens
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generated_tokens = outputs[0][input_ids.shape[-1]:].shape[0]
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# Calculate the total time taken
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total_time = end_time - start_time
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# Calculate tokens generated per second
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tokens_per_second = generated_tokens / total_time
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print(f"\nGenerated {generated_tokens} tokens in total, took {total_time:.2f} seconds, generating {tokens_per_second:.2f} tokens per second.")
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```
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## Evaluations
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The following data has been re-evaluated and calculated as the average for each test.
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| 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 |
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|-------------|----------------|----------------------------------------|--------------------------|--------------------------|--------------------------|--------------------------|--------------------------|
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| IF_Eval | 82.09 | 76.29 | 82.44 | 82.93 | **83.10** | 82.94 | 82.03 |
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| MMLU Pro | **35.87** | 33.1 | 35.65 | 35.32 | 34.91 | 34.5 | 33.96 |
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| TruthfulQA | **64.35** | 53.25 | 62.67 | 61.04 | 59.09 | 57.8 | 56.75 |
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| BBH | **49.48** | 44.87 | 48.86 | 48.47 | 48.30 | 48.19 | 47.93 |
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| GPQA | 31.98 | 29.50 | 32.25 | 32.38 | **32.61** | 31.14 | 30.6 |
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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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