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