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intervitens/
internlm2-limarp-lora

Name: internlm2-limarp-lora
Availability: InStock

peft

llama

generated_from_trainer

intervitens/internlm2-base-20b-llama

adapter

8-bit

bitsandbytes

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Don't use this yet, there's a problem with the llamafied internlm2 tokenizer. Prompt format: ChatML.

See axolotl config

axolotl version: 0.3.0

yaml

base_model: /data/internlm2-base-20b-llama
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: /data/train-all-8k.jsonl
    type: completion
dataset_prepared_path:
val_set_size: 0.05
output_dir: /data/internlm-limarp-lora-out

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 128
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00002

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

gradient_checkpointing: true
gradient_checkpointing_kwargs: 
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

internlm-limarp-lora

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.1216

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: True
load_in_4bit: False
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: fp4
bnb_4bit_use_double_quant: False
bnb_4bit_compute_dtype: float32

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
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: 10
num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss
2.3563	0.01	1	2.3995
2.1815	0.25	37	2.2693
2.1364	0.51	74	2.1684
2.1355	0.76	111	2.1526
2.1624	1.03	148	2.1435
2.1326	1.28	185	2.1367
1.9987	1.54	222	2.1330
2.0494	1.79	259	2.1291
2.0505	2.04	296	2.1266
2.075	2.3	333	2.1243
2.0183	2.55	370	2.1229
2.1047	2.81	407	2.1227
2.1309	3.06	444	2.1218
2.1249	3.31	481	2.1214
2.1423	3.57	518	2.1214
2.0913	3.82	555	2.1216

Framework versions

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

intervitens/internlm2-limarp-lora

internlm-limarp-lora

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

intervitens/
internlm2-limarp-lora