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Mathmate-7B-DELLA

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AI-MO/NuminaMath-7B-TIR
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Mathmate-7B-DELLA
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---
base_model:
- AI-MO/NuminaMath-7B-TIR
- deepseek-ai/DeepSeek-Prover-V1.5-RL
tags:
- merge
- mergekit
- lazymergekit
- AI-MO/NuminaMath-7B-TIR
- deepseek-ai/DeepSeek-Prover-V1.5-RL
license: apache-2.0
model-index:
  - name: Mathmate-7B-DELLA
    results:
      - task:
          type: text-generation
        dataset:
          name: AGIEval
          type: AGIEval
        metrics:
          - name: AGIEval
            type: AGIEval
            value: 21.95
      - task:
          type: text-generation
        dataset:
          name: GPT4All
          type: GPT4All
        metrics:
          - name: GPT4All
            type: GPT4All
            value: 36.5
      - task:
          type: text-generation
        dataset:
          name: TruthfulQA
          type: TruthfulQA
        metrics:
          - name: TruthfulQA
            type: TruthfulQA
            value: 48.08
      - task:
          type: text-generation
        dataset:
          name: Bigbench
          type: Bigbench
        metrics:
          - name: Bigbench
            type: Bigbench
            value: 28.89
---

# Mathmate-7B-DELLA

Mathmate-7B-DELLA is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [AI-MO/NuminaMath-7B-TIR](https://huggingface.co/AI-MO/NuminaMath-7B-TIR)
* [deepseek-ai/DeepSeek-Prover-V1.5-RL](https://huggingface.co/deepseek-ai/DeepSeek-Prover-V1.5-RL)

## 🧩 Configuration

```yaml
models:
  - model: AI-MO/NuminaMath-7B-TIR
    parameters:
      density: 0.5
      weight: 0.3
  - model: deepseek-ai/DeepSeek-Prover-V1.5-RL
    parameters:
      density: 0.5
      weight: 0.2
merge_method: della
base_model: deepseek-ai/deepseek-math-7b-base
parameters:
  normalize: true
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Haleshot/Mathmate-7B-DELLA"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```

## 📊 Evaluation Results

Evaluation results using LLMAutoeval:

| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|-------|---------|---------|------------|----------|---------|
| [Mathmate-7B-DELLA](https://huggingface.co/Haleshot/Mathmate-7B-DELLA) | 21.95 | 36.5 | 48.08 | 28.89 | 33.86 |

### AGIEval
| Task | Version | Metric | Value | Stderr |
|------|---------|--------|-------|--------|
| agieval_aqua_rat | 0 | acc | 21.26 | 2.57 |
| | | acc_norm | 22.05 | 2.61 |
| agieval_logiqa_en | 0 | acc | 20.89 | 1.59 |
| | | acc_norm | 25.65 | 1.71 |
| agieval_lsat_ar | 0 | acc | 21.74 | 2.73 |
| | | acc_norm | 19.57 | 2.62 |
| agieval_lsat_lr | 0 | acc | 13.92 | 1.53 |
| | | acc_norm | 18.82 | 1.73 |
| agieval_lsat_rc | 0 | acc | 21.19 | 2.50 |
| | | acc_norm | 18.96 | 2.39 |
| agieval_sat_en | 0 | acc | 24.76 | 3.01 |
| | | acc_norm | 21.36 | 2.86 |
| agieval_sat_en_without_passage | 0 | acc | 27.18 | 3.11 |
| | | acc_norm | 23.30 | 2.95 |
| agieval_sat_math | 0 | acc | 25.45 | 2.94 |
| | | acc_norm | 25.91 | 2.96 |

Average: 21.95%

### GPT4All
| Task | Version | Metric | Value | Stderr |
|------|---------|--------|-------|--------|
| arc_challenge | 0 | acc | 22.61 | 1.22 |
| | | acc_norm | 25.68 | 1.28 |
| arc_easy | 0 | acc | 25.25 | 0.89 |
| | | acc_norm | 25.08 | 0.89 |
| boolq | 1 | acc | 52.02 | 0.87 |
| hellaswag | 0 | acc | 25.77 | 0.44 |
| | | acc_norm | 26.09 | 0.44 |
| openbookqa | 0 | acc | 18.40 | 1.73 |
| | | acc_norm | 28.80 | 2.03 |
| piqa | 0 | acc | 51.31 | 1.17 |
| | | acc_norm | 50.11 | 1.17 |
| winogrande | 0 | acc | 47.75 | 1.40 |

Average: 36.5%

### TruthfulQA
| Task | Version | Metric | Value | Stderr |
|------|---------|--------|-------|--------|
| truthfulqa_mc | 1 | mc1 | 22.77 | 1.47 |
| | | mc2 | 48.08 | 1.70 |

Average: 48.08%

### Bigbench
| Task | Version | Metric | Value | Stderr |
|------|---------|--------|-------|--------|
| bigbench_causal_judgement | 0 | multiple_choice_grade | 49.47 | 3.64 |
| bigbench_date_understanding | 0 | multiple_choice_grade | 13.55 | 1.78 |
| bigbench_disambiguation_qa | 0 | multiple_choice_grade | 30.23 | 2.86 |
| bigbench_geometric_shapes | 0 | multiple_choice_grade | 10.03 | 1.59 |
| | | exact_str_match | 0.00 | 0.00 |
| bigbench_logical_deduction_five_objects | 0 | multiple_choice_grade | 19.40 | 1.77 |
| bigbench_logical_deduction_seven_objects | 0 | multiple_choice_grade | 14.00 | 1.31 |
| bigbench_logical_deduction_three_objects | 0 | multiple_choice_grade | 36.67 | 2.79 |
| bigbench_movie_recommendation | 0 | multiple_choice_grade | 23.60 | 1.90 |
| bigbench_navigate | 0 | multiple_choice_grade | 47.10 | 1.58 |
| bigbench_reasoning_about_colored_objects | 0 | multiple_choice_grade | 13.05 | 0.75 |
| bigbench_ruin_names | 0 | multiple_choice_grade | 53.79 | 2.36 |
| bigbench_salient_translation_error_detection | 0 | multiple_choice_grade | 15.63 | 1.15 |
| bigbench_snarks | 0 | multiple_choice_grade | 46.96 | 3.72 |
| bigbench_sports_understanding | 0 | multiple_choice_grade | 49.70 | 1.59 |
| bigbench_temporal_sequences | 0 | multiple_choice_grade | 25.80 | 1.38 |
| bigbench_tracking_shuffled_objects_five_objects | 0 | multiple_choice_grade | 19.76 | 1.13 |
| bigbench_tracking_shuffled_objects_seven_objects | 0 | multiple_choice_grade | 14.69 | 0.85 |
| bigbench_tracking_shuffled_objects_three_objects | 0 | multiple_choice_grade | 36.67 | 2.79 |

Average: 28.89%

Average score: 33.86%

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