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--- |
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base_model: teknium/OpenHermes-2.5-Mistral-7B |
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tags: |
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- mistral |
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- instruct |
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- finetune |
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- chatml |
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- gpt4 |
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- synthetic data |
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- distillation |
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- dpo |
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- rlhf |
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- laser |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- mlabonne/chatml_dpo_pairs |
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--- |
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<center><img src="https://i.imgur.com/gUlEJuU.jpeg"></center> |
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# NeuralHermes 2.5 - Mistral 7B - LASER |
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This is an experimental LASER version of NeuralHermes using [laserRMT](https://github.com/cognitivecomputations/laserRMT), based on [this paper](https://arxiv.org/pdf/2312.13558.pdf). |
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |
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|------------------------------------------------------------------------------------------------------|------:|------:|---------:|-------:|------:| |
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|[NeuralHermes-2.5-Mistral-7B-laser](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B-laser)| 43.54| 73.44| 55.26| 42.24| 53.62| |
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|[NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) | 43.67| 73.24| 55.37| 41.76| 53.51| |
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Fernando Fernandes Neto and Eric Hartford. "Optimizing Large Language Models Using Layer-Selective Rank Reduction and Random Matrix Theory." 2024. |
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NeuralHermes is an [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. It surpasses the original model on several benchmarks (see results). |
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It is directly inspired by the RLHF process described by [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template. |
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The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour. |
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## Results |
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### AGIEval |
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| Task |Version| Metric |Value| |Stderr| |
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|------------------------------|------:|--------|----:|---|-----:| |
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|agieval_aqua_rat | 0|acc |21.26|± | 2.57| |
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| | |acc_norm|22.83|± | 2.64| |
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|agieval_logiqa_en | 0|acc |39.32|± | 1.92| |
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| | |acc_norm|40.71|± | 1.93| |
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|agieval_lsat_ar | 0|acc |25.65|± | 2.89| |
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| | |acc_norm|25.65|± | 2.89| |
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|agieval_lsat_lr | 0|acc |48.82|± | 2.22| |
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| | |acc_norm|50.00|± | 2.22| |
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|agieval_lsat_rc | 0|acc |58.36|± | 3.01| |
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| | |acc_norm|57.25|± | 3.02| |
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|agieval_sat_en | 0|acc |74.27|± | 3.05| |
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| | |acc_norm|73.30|± | 3.09| |
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|agieval_sat_en_without_passage| 0|acc |43.69|± | 3.46| |
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| | |acc_norm|42.23|± | 3.45| |
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|agieval_sat_math | 0|acc |37.27|± | 3.27| |
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| | |acc_norm|36.36|± | 3.25| |
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Average: 43.54% |
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### GPT4All |
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| Task |Version| Metric |Value| |Stderr| |
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|-------------|------:|--------|----:|---|-----:| |
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|arc_challenge| 0|acc |57.76|± | 1.44| |
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| | |acc_norm|60.32|± | 1.43| |
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|arc_easy | 0|acc |83.84|± | 0.76| |
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| | |acc_norm|81.10|± | 0.80| |
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|boolq | 1|acc |86.70|± | 0.59| |
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|hellaswag | 0|acc |63.15|± | 0.48| |
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| | |acc_norm|82.55|± | 0.38| |
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|openbookqa | 0|acc |34.40|± | 2.13| |
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| | |acc_norm|45.20|± | 2.23| |
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|piqa | 0|acc |81.94|± | 0.90| |
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| | |acc_norm|82.97|± | 0.88| |
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|winogrande | 0|acc |75.22|± | 1.21| |
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Average: 73.44% |
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### TruthfulQA |
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| Task |Version|Metric|Value| |Stderr| |
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|-------------|------:|------|----:|---|-----:| |
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|truthfulqa_mc| 1|mc1 |37.70|± | 1.70| |
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| | |mc2 |55.26|± | 1.52| |
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Average: 55.26% |
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### Bigbench |
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| Task |Version| Metric |Value| |Stderr| |
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|------------------------------------------------|------:|---------------------|----:|---|-----:| |
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|bigbench_causal_judgement | 0|multiple_choice_grade|53.16|± | 3.63| |
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|bigbench_date_understanding | 0|multiple_choice_grade|65.31|± | 2.48| |
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|34.11|± | 2.96| |
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|bigbench_geometric_shapes | 0|multiple_choice_grade|27.02|± | 2.35| |
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| | |exact_str_match | 0.28|± | 0.28| |
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|27.80|± | 2.01| |
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|19.86|± | 1.51| |
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|48.33|± | 2.89| |
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|bigbench_movie_recommendation | 0|multiple_choice_grade|41.40|± | 2.20| |
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|bigbench_navigate | 0|multiple_choice_grade|50.00|± | 1.58| |
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|65.00|± | 1.07| |
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|bigbench_ruin_names | 0|multiple_choice_grade|46.21|± | 2.36| |
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|27.25|± | 1.41| |
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|bigbench_snarks | 0|multiple_choice_grade|70.72|± | 3.39| |
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|bigbench_sports_understanding | 0|multiple_choice_grade|65.72|± | 1.51| |
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|bigbench_temporal_sequences | 0|multiple_choice_grade|30.40|± | 1.46| |
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|22.56|± | 1.18| |
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|17.09|± | 0.90| |
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|48.33|± | 2.89| |
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Average: 42.24% |
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Average score: 53.62% |
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## Usage |
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You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend. |
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You can also run this model using the following code: |
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```python |
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import transformers |
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from transformers import AutoTokenizer |
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# Format prompt |
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message = [ |
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{"role": "system", "content": "You are a helpful assistant chatbot."}, |
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{"role": "user", "content": "What is a Large Language Model?"} |
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] |
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tokenizer = AutoTokenizer.from_pretrained(new_model) |
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prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False) |
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# Create pipeline |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model="mlabonne/NeuralHermes-2.5-Mistral-7B-laser", |
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tokenizer=tokenizer |
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) |
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# Generate text |
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sequences = pipeline( |
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prompt, |
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do_sample=True, |
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temperature=0.7, |
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top_p=0.9, |
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num_return_sequences=1, |
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max_length=200, |
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) |
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print(sequences[0]['generated_text']) |
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``` |
