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Model: oobabooga/CodeBooga-34B-v0.1 Source: Original Platform
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README.md
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---
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license: llama2
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---
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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>
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config.json
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{
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"_name_or_path": "CodeBooga-34B-v0.1",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"bos_token_id": 1,
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"hidden_act": "silu",
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"initializer_range": 0.02,
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"intermediate_size": 22016,
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"max_position_embeddings": 16384,
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"model_type": "llama",
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"num_attention_heads": 64,
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"num_hidden_layers": 48,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.34.0",
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"use_cache": true,
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"vocab_size": 32000
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}
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6
special_tokens_map.json
Normal file
6
special_tokens_map.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
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||||
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||||
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||||
93391
tokenizer.json
Normal file
93391
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
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Normal file
@@ -0,0 +1,3 @@
|
||||
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43
tokenizer_config.json
Normal file
43
tokenizer_config.json
Normal file
@@ -0,0 +1,43 @@
|
||||
{
|
||||
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||||
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"use_default_system_prompt": true
|
||||
}
|
||||
Reference in New Issue
Block a user