File size: 4,637 Bytes
9dabfbb
973a01c
 
 
 
 
37281f2
 
 
 
 
973a01c
37281f2
 
9dabfbb
973a01c
37281f2
 
ea325c0
37281f2
 
 
 
 
 
 
 
 
9dabfbb
973a01c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
---
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- jondurbin/airoboros-2.2
- Open-Orca/OpenOrca
- garage-bAInd/Open-Platypus
- WizardLM/WizardLM_evol_instruct_V2_196k
- TokenBender/python_eval_instruct_51k
tags:
- llama-2
- code
license: llama2
model-index:
- name: SpeechlessCoder
  results:
  - task:
      type: text-generation
    dataset:
      type: openai_humaneval
      name: HumanEval
    metrics:
    - name: pass@1
      type: pass@1
      value: 52.439
      verified: false
---

<p><h1> speechless-coding-7b-16k-tora  </h1></p>

Use the following dataset to fine-tune llm_agents/tora-code-7b-v1.0 in order to improve the model's reasoning and planning abilities.

context window length: 16,384
prompt_type = "alpaca"
max_tokens > 128 && < 16384
>
Total 177,333 samples 316 MB
- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 21,923 samples.
- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 62,973 samples.
- garage-bAInd/Open-Platypus: 100%, 22,760 samples.
- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,081 samples
- TokenBender/python_eval_instruct_51k: “python” in output .39,596 samples


50 samples/T=0.2/MaxTokens=512/Top_P=0.95

Code: https://github.com/uukuguy/speechless

## HumanEval

| Metric | Value |
| --- | --- |
| humaneval-python | 52.44 |

[Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard)

CodeLlama-34B-Python: 53.29

CodeLlama-34B-Instruct: 50.79

                        CodeLlama-13B-Instruct: 50.6

                                                CodeLlama-34B: 45.11

                                                CodeLlama-13B-Python: 42.89

                                                CodeLlama-13B: 35.07

## MultiPL-E

                                                | Metric | Value |
                                                | --- | --- |
                                                | python | 55.96 |
                                                | java | 37.84 |
                                                | javascript | 46.93 |
                                                | cpp | 37.48 |
                                                | rust | 29.01 |
                                                | go | 28.99 |
                                                |  sh | 12.11 |
                                                | julia | 31.47 |
                                                | typescript | 47.80 |

## LMEval

                                                [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
                                                | Metric | Value |
                                                | --- | --- |
                                                | ARC | |
                                                | HellaSwag | |
                                                | MMLU | |
                                                | TruthfulQA |  |
                                                | Average |  |

## Parameters

                                                | | |
                                                |------ | ------ |
                                                | lr | 2e-4 |
                                                | lr_scheduler_type | cosine |
                                                | weight_decay | 0.0 |
                                                | optim | paged_adamw_8bit |
                                                | flash_attention | True |
                                                | rerope | False |
                                                | max_new_tokens | 16384 |
                                                | num_train_epochs | 2 |
                                                | bits | 4 |
                                                | lora_r | 64 |
                                                | lora_alpha | 256 |
                                                | lora_dropout | 0.05 |
                                                | double_quant | True |
                                                | quant_type | nf4 |
                                                | dataset_format | sharegpt |
                                                | mini_batch_size | 2 |
                                                | grandient_accumulation_steps | 32 |
                                                | bf16 | True |

                                                A100-40G x 4