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---
license: llama3.1
language:
- en
library_name: transformers
tags:
- mergekit
- merge
base_model:
- meta-llama/Meta-Llama-3.1-70B-Instruct
- turboderp/Cat-Llama-3-70B-instruct
- Nexusflow/Athene-70B
---
**Cathallama-70B-128K** **originally from: [gbueno86/Cathallama-70B](https://huggingface.co/gbueno86/Cathallama-70B)**
=====================================
Awesome model, my new daily driver.
Edit: I am seeing a lot of token generations pointing to unknown unicode addresses that didn't show up during testing for this model, so I have stopped using it and I am working on a new version.
**Notable Performance**
* 9% overall success rate increase on MMLU-PRO over LLaMA 3.1 70b at Q4_0
* Strong performance in MMLU-PRO categories overall
* Great performance during manual testing
**Creation workflow**
=====================
**Models merged**
* meta-llama/Meta-Llama-3.1-70B-Instruct
* turboderp/Cat-Llama-3-70B-instruct
* Nexusflow/Athene-70B
```
flowchart TD
A[Nexusflow_Athene] -->|Merge with| B[Meta-Llama-3.1]
C[turboderp_Cat] -->|Merge with| D[Meta-Llama-3.1]
B -->| | E[Merge]
D -->| | E[Merge]
E[Merge] -->|Result| F[Cathallama]
```
**Testing**
=====================
**Hyperparameters**
---------------
* **Temperature**: 0.0 for automated, 0.9 for manual
* **Penalize repeat sequence**: 1.05
* **Consider N tokens for penalize**: 256
* **Penalize repetition of newlines**
* **Top-K sampling**: 40
* **Top-P sampling**: 0.95
* **Min-P sampling**: 0.05
**LLaMAcpp Version**
------------------
* b3527-2-g2d5dd7bb
* -fa -ngl -1 -ctk f16 --no-mmap
**Tested Files**
------------------
* Cathallama-70B.Q4_0.gguf
* Nexusflow_Athene-70B.Q4_0.gguf
* turboderp_Cat-Llama-3-70B-instruct.Q4_0.gguf
* Meta-Llama-3.1-70B-Instruct.Q4_0.gguf
**Tests**
--------------
**Manual testing**
| Category | Test Case | Cathallama-70B.Q4_0.gguf | Nexusflow_Athene-70B.Q4_0.gguf | turboderp_Cat-Llama-3-70B-instruct.Q4_0.gguf | Meta-Llama-3.1-70B-Instruct.Q4_0.gguf |
| --- | --- | --- | --- | --- | --- |
| **Common Sense** | Ball on cup | OK | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> | OK |
| | Big duck small horse | <span style="color: red;">KO</span> | OK | <span style="color: red;">KO</span> | OK |
| | Killers | OK | OK | <span style="color: red;">KO</span> | OK |
| | Strawberry r's | OK | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> |
| | 9.11 or 9.9 bigger | <span style="color: red;">KO</span> | OK | OK | <span style="color: red;">KO</span> |
| | Dragon or lens | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> |
| | Shirts | OK | OK | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> |
| | Sisters | OK | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> |
| | Jane faster | OK | OK | OK | OK |
| **Programming** | JSON | OK | OK | OK | OK |
| | Python snake game | OK | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> |
| **Math** | Door window combination | OK | OK | <span style="color: red;">KO</span> | <span style="color: red;">KO</span> |
| **Smoke** | Poem | OK | OK | OK | OK |
| | Story | OK | OK | KO | OK |
*Note: See [sample_generations.txt](https://huggingface.co/gbueno86/Cathallama-70B/blob/main/sample_generations.txt) on the main folder of the repo for the raw generations.*
**MMLU-PRO**
| Model | Success % |
| --- | --- |
| Cathallama-70B.Q4_0.gguf | **51.0%** |
| turboderp_Cat-Llama-3-70B-instruct.Q4_0.gguf | 37.0% |
| Nexusflow_Athene-70B.Q4_0.gguf | 41.0% |
| Meta-Llama-3.1-70B-Instruct.Q4_0.gguf | 42.0% |
| MMLU-PRO category| Cathallama-70B.Q4_0.gguf | Nexusflow_Athene-70B.Q4_0.gguf | turboderp_Cat-Llama-3-70B-instruct.Q4_0.gguf | Meta-Llama-3.1-70B-Instruct.Q4_0.gguf |
| --- | --- | --- | --- | --- |
| Business | **50.0%** | 45.0% | 20.0% | 40.0% |
| Law | **40.0%** | 30.0% | 30.0% | 35.0% |
| Psychology | **85.0%** | 80.0% | 70.0% | 75.0% |
| Biology | 80.0% | 70.0% | **85.0%** | 80.0% |
| Chemistry | **55.0%** | 40.0% | 35.0% | 35.0% |
| History | **65.0%** | 60.0% | 55.0% | **65.0%** |
| Other | **55.0%** | 50.0% | 45.0% | 50.0% |
| Health | **75.0%** | 40.0% | 60.0% | 65.0% |
| Economics | **80.0%** | 75.0% | 65.0% | 70.0% |
| Math | **45.0%** | 35.0% | 15.0% | 40.0% |
| Physics | **50.0%** | 45.0% | 45.0% | 45.0% |
| Computer Science | **60.0%** | 55.0% | 55.0% | **60.0%** |
| Philosophy | 55.0% | **60.0%** | 45.0% | 50.0% |
| Engineering | 35.0% | **40.0%** | 25.0% | 35.0% |
*Note: MMLU-PRO Overall tested with 100 questions. Categories testes with 20 questions from each category.*
**PubmedQA**
Model Name | Success% |
| --- | --- |
| Cathallama-70B.Q4_0.gguf| 73.00% |
| turboderp_Cat-Llama-3-70B-instruct.Q4_0.gguf | **76.00%** |
| Nexusflow_Athene-70B.Q4_0.gguf | 67.00% |
| Meta-Llama-3.1-70B-Instruct.Q4_0.gguf | 72.00% |
**Request**
--------------
If you are hiring in the EU or can sponsor a visa, PM me :D
PS. Thank you mradermacher for the GGUFs! |