metadata
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: 0
verified: false
speechless-code-mistral-7b-v1.0
Use the following dataset to fine-tune llm_agents/Mistral-7B-v0.1 in order to improve the model's reasoning and planning abilities.
Total 201,981 samples.
- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples.
- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples.
- garage-bAInd/Open-Platypus: 100%, 24,926 samples.
- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples
- TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples
- Spider: 8,659 samples
lr | 2e-4 |
lr_scheduler_type | cosine |
weight_decay | 0.0 |
optim | paged_adamw_8bit |
flash_attention | True |
rerope | False |
max_new_tokens | 4096 |
num_train_epochs | 2 |
bits | 4 |
lora_r | 64 |
lora_alpha | 16 |
lora_dropout | 0.05 |
double_quant | True |
quant_type | nf4 |
dataset_format | airoboros |
mini_batch_size | 2 |
grandient_accumulation_steps | 32 |
bf16 | True |
A100-40G x 4
epoch | 2.0 |
etrain_loss | 0.4708 |
etrain_runtime | 12:12:53.64 |
etrain_samples_per_second | 9.002 |
etrain_steps_per_second | 0.07 |
eeval_loss | 0.4851 |
eeval_runtime | 0:00:10.31 |
eeval_samples_per_second | 19.385 |
eeval_steps_per_second | 4.846 |
Metric | Value |
---|---|
humaneval-python | 46.341 |
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
Metric | Value |
---|---|
ARC | |
HellaSwag | |
MMLU | |
TruthfulQA | |
Average |