94 lines
2.7 KiB
Markdown
94 lines
2.7 KiB
Markdown
|
---
|
||
|
|
license: llama2
|
||
|
|
---
|
||
|
|
|
||
|
|
# CodeBooga-34B-v0.1
|
||
|
|
|
||
|
|
This is a merge between the following two models:
|
||
|
|
|
||
|
|
1) [Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2)
|
||
|
|
2) [WizardCoder-Python-34B-V1.0](https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0)
|
||
|
|
|
||
|
|
It was created with the [BlockMerge Gradient script](https://github.com/Gryphe/BlockMerge_Gradient), the same one that was used to create [MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b), and with the same settings. The following YAML was used:
|
||
|
|
|
||
|
|
```yaml
|
||
|
|
model_path1: "Phind_Phind-CodeLlama-34B-v2_safetensors"
|
||
|
|
model_path2: "WizardLM_WizardCoder-Python-34B-V1.0_safetensors"
|
||
|
|
output_model_path: "CodeBooga-34B-v0.1"
|
||
|
|
operations:
|
||
|
|
- operation: lm_head # Single tensor
|
||
|
|
filter: "lm_head"
|
||
|
|
gradient_values: [0.75]
|
||
|
|
- operation: embed_tokens # Single tensor
|
||
|
|
filter: "embed_tokens"
|
||
|
|
gradient_values: [0.75]
|
||
|
|
- operation: self_attn
|
||
|
|
filter: "self_attn"
|
||
|
|
gradient_values: [0.75, 0.25]
|
||
|
|
- operation: mlp
|
||
|
|
filter: "mlp"
|
||
|
|
gradient_values: [0.25, 0.75]
|
||
|
|
- operation: layernorm
|
||
|
|
filter: "layernorm"
|
||
|
|
gradient_values: [0.5, 0.5]
|
||
|
|
- operation: modelnorm # Single tensor
|
||
|
|
filter: "model.norm"
|
||
|
|
gradient_values: [0.75]
|
||
|
|
```
|
||
|
|
|
||
|
|
## Prompt format
|
||
|
|
|
||
|
|
Both base models use the Alpaca format, so it should be used for this one as well.
|
||
|
|
|
||
|
|
```
|
||
|
|
Below is an instruction that describes a task. Write a response that appropriately completes the request.
|
||
|
|
|
||
|
|
### Instruction:
|
||
|
|
Your instruction
|
||
|
|
|
||
|
|
### Response:
|
||
|
|
Bot reply
|
||
|
|
|
||
|
|
### Instruction:
|
||
|
|
Another instruction
|
||
|
|
|
||
|
|
### Response:
|
||
|
|
Bot reply
|
||
|
|
```
|
||
|
|
|
||
|
|
## Evaluation
|
||
|
|
|
||
|
|
(This is not very scientific, so bear with me.)
|
||
|
|
|
||
|
|
I made a quick experiment where I asked a set of 3 Python and 3 Javascript questions (real world, difficult questions with nuance) to the following models:
|
||
|
|
|
||
|
|
1) This one
|
||
|
|
2) A second variant generated with `model_path1` and `model_path2` swapped in the YAML above, which I called CodeBooga-Reversed-34B-v0.1
|
||
|
|
3) WizardCoder-Python-34B-V1.0
|
||
|
|
4) Phind-CodeLlama-34B-v2
|
||
|
|
|
||
|
|
Specifically, I used 4.250b EXL2 quantizations of each. I then sorted the responses for each question by quality, and attributed the following scores:
|
||
|
|
|
||
|
|
* 4th place: 0
|
||
|
|
* 3rd place: 1
|
||
|
|
* 2nd place: 2
|
||
|
|
* 1st place: 4
|
||
|
|
|
||
|
|
The resulting cumulative scores were:
|
||
|
|
|
||
|
|
* CodeBooga-34B-v0.1: 22
|
||
|
|
* WizardCoder-Python-34B-V1.0: 12
|
||
|
|
* Phind-CodeLlama-34B-v2: 7
|
||
|
|
* CodeBooga-Reversed-34B-v0.1: 1
|
||
|
|
|
||
|
|
CodeBooga-34B-v0.1 performed very well, while its variant performed poorly, so I uploaded the former but not the latter.
|
||
|
|
|
||
|
|
## Quantized versions
|
||
|
|
|
||
|
|
### GGUF
|
||
|
|
|
||
|
|
TheBloke has kindly provided GGUF quantizations for llama.cpp:
|
||
|
|
|
||
|
|
https://huggingface.co/TheBloke/CodeBooga-34B-v0.1-GGUF
|
||
|
|
|
||
|
|
<a href="https://ko-fi.com/oobabooga"><img src="https://i.imgur.com/UJlEAYw.png"></a>
|