Text Generation
Transformers
PyTorch
English
llama
llama-2
code
Eval Results
Inference Endpoints
text-generation-inference
File size: 2,759 Bytes
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---
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

## 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