EvolCodeLlama-7b

feulf/EvolCodeLlama-7b

EvolCodeLlama-7b is a machine learning model from feulf. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as llama2.

should probably proofread and complete it, then remove this comment. --

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At a glance

Library: peft
License: llama2
Model type: llama
Access: Public
Created: Jan 18, 2024
Updated: Jan 18, 2024
SHA: 637bdaea

Base models

codellama/CodeLlama-7b-hf

Model card

From the Hugging Face model README

<details><summary>See axolotl config</summary>

axolotl version: 0.3.0

base_model: codellama/CodeLlama-7b-hf
base_model_config: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
hub_model_id: EvolCodeLlama-7b

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: mlabonne/Evol-Instruct-Python-1k
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.02
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 2048
sample_packing: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

</details><br>

EvolCodeLlama-7b

This model is a fine-tuned version of codellama/CodeLlama-7b-hf on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3797

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

The following bitsandbytes quantization config was used during training:

quant_method: bitsandbytes
load_in_8bit: False
load_in_4bit: True
llm_int8_threshold: 6.0
llm_int8_skip_modules: None
llm_int8_enable_fp32_cpu_offload: False
llm_int8_has_fp16_weight: False
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: True
bnb_4bit_compute_dtype: bfloat16

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.3472	0.01	1	0.4986
0.3139	0.03	4	0.4985
0.2981	0.07	8	0.4983
0.4311	0.1	12	0.4979
0.3958	0.14	16	0.4960
0.335	0.17	20	0.4915
0.4286	0.2	24	0.4808
0.4011	0.24	28	0.4629
0.3269	0.27	32	0.4445
0.2559	0.31	36	0.4284
0.3786	0.34	40	0.4174
0.2967	0.37	44	0.4107
0.2677	0.41	48	0.4027
0.2455	0.44	52	0.3959
0.3267	0.47	56	0.3916
0.2902	0.51	60	0.3882
0.1845	0.54	64	0.3878
0.2593	0.58	68	0.3869
0.3104	0.61	72	0.3836
0.3799	0.64	76	0.3819
0.2059	0.68	80	0.3794
0.3177	0.71	84	0.3792
0.2307	0.75	88	0.3768
0.282	0.78	92	0.3749
0.2713	0.81	96	0.3738
0.2948	0.85	100	0.3725
0.2311	0.88	104	0.3713
0.2516	0.92	108	0.3716
0.2462	0.95	112	0.3715
0.2035	0.98	116	0.3711
0.2638	1.02	120	0.3712
0.2477	1.05	124	0.3726
0.1986	1.08	128	0.3682
0.2292	1.12	132	0.3671
0.1549	1.15	136	0.3680
0.1953	1.19	140	0.3683
0.224	1.22	144	0.3671
0.1941	1.25	148	0.3687
0.2234	1.29	152	0.3709
0.2659	1.32	156	0.3700
0.2535	1.36	160	0.3689
0.2115	1.39	164	0.3683
0.2481	1.42	168	0.3693
0.2101	1.46	172	0.3699
0.228	1.49	176	0.3697
0.3159	1.53	180	0.3680
0.2257	1.56	184	0.3664
0.1684	1.59	188	0.3670
0.2277	1.63	192	0.3663
0.2787	1.66	196	0.3668
0.2284	1.69	200	0.3654
0.2789	1.73	204	0.3640
0.2089	1.76	208	0.3632
0.3387	1.8	212	0.3633
0.2677	1.83	216	0.3610
0.2684	1.86	220	0.3609
0.2458	1.9	224	0.3610
0.2808	1.93	228	0.3602
0.2895	1.97	232	0.3596
0.323	2.0	236	0.3591
0.2105	2.03	240	0.3623
0.1911	2.07	244	0.3720
0.2888	2.1	248	0.3802
0.1958	2.13	252	0.3748
0.1785	2.17	256	0.3701
0.2604	2.2	260	0.3709
0.2212	2.24	264	0.3737
0.1996	2.27	268	0.3772
0.1567	2.3	272	0.3778
0.1777	2.34	276	0.3778
0.2642	2.37	280	0.3785
0.1907	2.4	284	0.3796
0.1637	2.44	288	0.3785
0.1778	2.47	292	0.3785
0.144	2.51	296	0.3789
0.1758	2.54	300	0.3788
0.2018	2.57	304	0.3784
0.3126	2.61	308	0.3783
0.1623	2.64	312	0.3790
0.223	2.68	316	0.3798
0.2109	2.71	320	0.3797
0.1606	2.74	324	0.3797
0.2226	2.78	328	0.3796
0.2068	2.81	332	0.3798
0.1547	2.85	336	0.3797
0.2513	2.88	340	0.3796
0.2688	2.91	344	0.3797
0.1481	2.95	348	0.3796
0.1443	2.98	352	0.3797

Framework versions

PEFT 0.7.0
Transformers 4.37.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.16.1
Tokenizers 0.15.0

Library: peft
Type: llama
License: llama2
Created: Jan 18, 2024
Updated: Jan 18, 2024