---
language:
- en
license: llama2
library_name: transformers
tags:
- llama-2
- code
datasets:
- jondurbin/airoboros-2.2
- Open-Orca/OpenOrca
- garage-bAInd/Open-Platypus
- WizardLM/WizardLM_evol_instruct_V2_196k
- TokenBender/python_eval_instruct_51k
pipeline_tag: text-generation
model-index:
- name: SpeechlessCoder
  results:
  - task:
      type: text-generation
    dataset:
      name: HumanEval
      type: openai_humaneval
    metrics:
    - type: pass@1
      value: 52.439
      name: pass@1
      verified: false
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 41.21
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-coding-7b-16k-tora
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 64.45
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-coding-7b-16k-tora
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 39.14
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-coding-7b-16k-tora
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 44.91
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-coding-7b-16k-tora
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 63.61
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-coding-7b-16k-tora
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 17.29
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=uukuguy/speechless-coding-7b-16k-tora
      name: Open LLM Leaderboard
---

<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

## How to Prompt the Model
This model accepts the Alpaca instruction format.

For example:
```
You are an intelligent programming assistant.

### Instruction:
Implement a linked list in C++

### Response:
```


## 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

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-coding-7b-16k-tora)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |45.10|
|AI2 Reasoning Challenge (25-Shot)|41.21|
|HellaSwag (10-Shot)              |64.45|
|MMLU (5-Shot)                    |39.14|
|TruthfulQA (0-shot)              |44.91|
|Winogrande (5-shot)              |63.61|
|GSM8k (5-shot)                   |17.29|