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--- |
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license: apache-2.0 |
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tags: |
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- merge |
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- mergekit |
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--- |
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# Marcoro14-7B-slerp |
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This model is a merge of the following models made with [mergekit](https://github.com/cg123/mergekit): |
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* [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3) |
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* [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1) |
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## 🏆 Evaluation |
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Marcoro14-7B-slerp is the second best-performing 7B LLM on the Open LLM Leaderboard: |
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I also evaluated it using Nous' benchmark suite and obtained the following results: |
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| Model |AGIEval|GPT4ALL|TruthfulQA|Bigbench|Average| |
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|-------------------------|------:|------:|---------:|-------:|------:| |
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|Marcoro14-7B-slerp | 44.66| 76.24| 64.15| 45.64| 57.67| |
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|OpenHermes-2.5-Mistral-7B| 43.07| 73.12| 53.04| 40.96| 52.57| |
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|Change | +1.59| +3.12| +11.11| +4.68| +5.1| |
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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 |26.38|± | 2.77| |
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| | |acc_norm|24.41|± | 2.70| |
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|agieval_logiqa_en | 0|acc |38.25|± | 1.91| |
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| | |acc_norm|39.32|± | 1.92| |
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|agieval_lsat_ar | 0|acc |24.35|± | 2.84| |
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| | |acc_norm|25.22|± | 2.87| |
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|agieval_lsat_lr | 0|acc |50.00|± | 2.22| |
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| | |acc_norm|50.59|± | 2.22| |
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|agieval_lsat_rc | 0|acc |62.83|± | 2.95| |
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| | |acc_norm|62.08|± | 2.96| |
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|agieval_sat_en | 0|acc |79.61|± | 2.81| |
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| | |acc_norm|79.61|± | 2.81| |
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|agieval_sat_en_without_passage| 0|acc |45.15|± | 3.48| |
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| | |acc_norm|45.63|± | 3.48| |
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|agieval_sat_math | 0|acc |33.18|± | 3.18| |
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| | |acc_norm|30.45|± | 3.11| |
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Average: 44.66% |
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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 |63.91|± | 1.40| |
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| | |acc_norm|64.93|± | 1.39| |
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|arc_easy | 0|acc |86.07|± | 0.71| |
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| | |acc_norm|83.75|± | 0.76| |
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|boolq | 1|acc |88.56|± | 0.56| |
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|hellaswag | 0|acc |67.31|± | 0.47| |
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| | |acc_norm|85.28|± | 0.35| |
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|openbookqa | 0|acc |36.40|± | 2.15| |
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| | |acc_norm|48.20|± | 2.24| |
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|piqa | 0|acc |82.59|± | 0.88| |
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| | |acc_norm|84.39|± | 0.85| |
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|winogrande | 0|acc |78.53|± | 1.15| |
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Average: 76.24% |
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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 |46.88|± | 1.75| |
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| | |mc2 |64.15|± | 1.52| |
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Average: 64.15% |
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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|56.32|± | 3.61| |
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|bigbench_date_understanding | 0|multiple_choice_grade|66.40|± | 2.46| |
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|bigbench_disambiguation_qa | 0|multiple_choice_grade|45.35|± | 3.11| |
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|bigbench_geometric_shapes | 0|multiple_choice_grade|20.33|± | 2.13| |
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| | |exact_str_match | 4.74|± | 1.12| |
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|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|30.00|± | 2.05| |
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|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|21.43|± | 1.55| |
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|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|52.33|± | 2.89| |
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|bigbench_movie_recommendation | 0|multiple_choice_grade|39.20|± | 2.19| |
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|bigbench_navigate | 0|multiple_choice_grade|53.90|± | 1.58| |
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|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|72.15|± | 1.00| |
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|bigbench_ruin_names | 0|multiple_choice_grade|52.46|± | 2.36| |
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|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|25.75|± | 1.38| |
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|bigbench_snarks | 0|multiple_choice_grade|72.38|± | 3.33| |
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|bigbench_sports_understanding | 0|multiple_choice_grade|73.63|± | 1.40| |
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|bigbench_temporal_sequences | 0|multiple_choice_grade|45.70|± | 1.58| |
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|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|23.44|± | 1.20| |
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|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|18.51|± | 0.93| |
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|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|52.33|± | 2.89| |
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Average: 45.64% |
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Average score: 57.67% |
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## 🧩 Configuration |
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```yaml |
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slices: |
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- sources: |
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- model: AIDC-ai-business/Marcoroni-7B-v3 |
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layer_range: [0, 32] |
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- model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1 |
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layer_range: [0, 32] |
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merge_method: slerp |
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base_model: AIDC-ai-business/Marcoroni-7B-v3 |
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parameters: |
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t: |
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- filter: self_attn |
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value: [0, 0.5, 0.3, 0.7, 1] |
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- filter: mlp |
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value: [1, 0.5, 0.7, 0.3, 0] |
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- value: 0.5 |
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dtype: bfloat16 |
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``` |
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## 💻 Usage |
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```python |
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!pip install -qU transformers accelerate |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "mlabonne/Marcoro14-7B-slerp" |
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messages = [{"role": "user", "content": "What is a large language model?"}] |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
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Output: |
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> A large language model is a type of artificial intelligence (AI) system that has been trained on vast amounts of text data. It's designed to understand and generate human-like language, making predictions on what words or phrases might come next in a sentence or document. These models use complex algorithms and neural network architectures to learn from the data and improve their performance over time. Some well-known large language models include GPT-3 from OpenAI and BERT from Google. |
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