YAML Metadata
Warning:
empty or missing yaml metadata in repo card
(https://huggingface.co/docs/hub/model-cards#model-card-metadata)
Quantization made by Richard Erkhov.
Magic-Dolphin-7b - GGUF
- Model creator: https://huggingface.co/InferenceIllusionist/
- Original model: https://huggingface.co/InferenceIllusionist/Magic-Dolphin-7b/
| Name | Quant method | Size |
|---|---|---|
| Magic-Dolphin-7b.Q2_K.gguf | Q2_K | 2.53GB |
| Magic-Dolphin-7b.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| Magic-Dolphin-7b.IQ3_S.gguf | IQ3_S | 2.96GB |
| Magic-Dolphin-7b.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| Magic-Dolphin-7b.IQ3_M.gguf | IQ3_M | 3.06GB |
| Magic-Dolphin-7b.Q3_K.gguf | Q3_K | 3.28GB |
| Magic-Dolphin-7b.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| Magic-Dolphin-7b.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| Magic-Dolphin-7b.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| Magic-Dolphin-7b.Q4_0.gguf | Q4_0 | 3.83GB |
| Magic-Dolphin-7b.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| Magic-Dolphin-7b.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| Magic-Dolphin-7b.Q4_K.gguf | Q4_K | 4.07GB |
| Magic-Dolphin-7b.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| Magic-Dolphin-7b.Q4_1.gguf | Q4_1 | 4.24GB |
| Magic-Dolphin-7b.Q5_0.gguf | Q5_0 | 4.65GB |
| Magic-Dolphin-7b.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| Magic-Dolphin-7b.Q5_K.gguf | Q5_K | 4.78GB |
| Magic-Dolphin-7b.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| Magic-Dolphin-7b.Q5_1.gguf | Q5_1 | 5.07GB |
| Magic-Dolphin-7b.Q6_K.gguf | Q6_K | 5.53GB |
| Magic-Dolphin-7b.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
license: apache-2.0 base_model: - cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser - Locutusque/Hyperion-1.5-Mistral-7B - ibm/merlinite-7b library_name: transformers tags: - mergekit - merge - code model-index: - name: Magic-Dolphin-7b results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 65.78 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.61 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 58.01 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 79.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 51.18 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InferenceIllusionist/Magic-Dolphin-7b name: Open LLM Leaderboard
Magic-Dolphin-7b
The follow-up to this model has been released, check out the updated benchmarks here for Excalibur-7b
For GGUF files please look here
A linear merge of:
- cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
- Locutusque/Hyperion-1.5-Mistral-7B
- ibm/merlinite-7b
These three models showed excellent acumen in technical topics so I wanted to see how they would behave together in a merge. Several different ratios were tested before this release, in the end a higher weighting for merlinite-7b helped smooth out some edges. This model is a test of how LAB tuning is impacted by merges with models leveraging DPO.
Benchmark Performance
| Name | Avg. | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|---|
| Magic-Dolphin-7b | 67.48 | 65.78 | 85.61 | 64.64 | 58.01 | 79.64 | 51.18 |
| dolphin-2.6-mistral-7b-dpo-laser | 67.28 | 66.3 | 85.73 | 63.16 | 61.71 | 79.16 | 47.61 |
| merlinite-7b | 64 | 63.65 | 84.52 | 64.91 | 50.15 | 79.72 | 41.09 |
| Hyperion-1.5-Mistral-7B | 61.43 | 60.49 | 83.64 | 63.57 | 41.78 | 78.61 | 40.49 |
This was my first experiment with merging models so any feedback is greatly appreciated.
Uses Alpaca template.
Sample Question
Merge Details
Merge Method
This model was merged using the linear merge method.
Models Merged
The following models were included in the merge:
- cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
- Locutusque/Hyperion-1.5-Mistral-7B
- ibm/merlinite-7b
Configuration
The following YAML configuration was used to produce this model:
models:
- model: models/dolphin-2.6-mistral-7b-dpo-laser
parameters:
weight: 1.0
- model: models/Hyperion-1.5-Mistral-7B
parameters:
weight: 0.3
- model: models/merlinite-7b
parameters:
weight: 0.5
merge_method: linear
dtype: float16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 67.48 |
| AI2 Reasoning Challenge (25-Shot) | 65.78 |
| HellaSwag (10-Shot) | 85.61 |
| MMLU (5-Shot) | 64.64 |
| TruthfulQA (0-shot) | 58.01 |
| Winogrande (5-shot) | 79.64 |
| GSM8k (5-shot) | 51.18 |
- Downloads last month
- 358