# CodeBooga-34B-v0.1

This is a merge between the following two models:

It was created with the BlockMerge Gradient script, the same one that was used to create MythoMax-L2-13b, and with the same settings. The following YAML was used:

```
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:

- This one
- A second variant generated with
`model_path1`

and`model_path2`

swapped in the YAML above, which I called CodeBooga-Reversed-34B-v0.1 - WizardCoder-Python-34B-V1.0
- 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:

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