metadata
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: 41.13
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=speechlessai/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: 64.48
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=speechlessai/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: 38.86
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=speechlessai/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: 44.95
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=speechlessai/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: 63.85
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=speechlessai/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: 17.06
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=speechlessai/speechless-coding-7b-16k-tora
name: Open LLM Leaderboard
speechless-coding-7b-16k-tora
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 |
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
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 45.05 |
| AI2 Reasoning Challenge (25-Shot) | 41.13 |
| HellaSwag (10-Shot) | 64.48 |
| MMLU (5-Shot) | 38.86 |
| TruthfulQA (0-shot) | 44.95 |
| Winogrande (5-shot) | 63.85 |
| GSM8k (5-shot) | 17.06 |