See axolotl config
axolotl version: 0.3.0
base_model: stabilityai/stablelm-3b-4e1t
base_model_config: stabilityai/stablelm-3b-4e1t
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: GPTNeoXTokenizerFast
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: totally-not-an-llm/EverythingLM-data-V3
type: alpaca
dataset_prepared_path:
val_set_size: 0.005
output_dir: ./everything
adapter: qlora
sequence_len: 1024
sample_packing: false
pad_to_sequence_len: true
save_safetensors: false
lora_r: 128
lora_alpha: 256
lora_dropout: 0.05
lora_target_linear: false
lora_fan_in_fan_out:
lora_modules_to_save:
- embed_tokens
- lm_head
lora_target_modules:
- q_proj
- v_proj
gradient_accumulation_steps: 1
micro_batch_size: 16
num_epochs: 5
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.00005
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: 10
evals_per_epoch: 1
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<|endoftext|>"
eos_token: "<|im_end|>"
unk_token: "<|endoftext|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
everything
This model is a fine-tuned version of stabilityai/stablelm-3b-4e1t on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8096
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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.3873 | 0.02 | 1 | 1.6256 |
| 1.1399 | 1.0 | 60 | 0.8263 |
| 1.5717 | 2.0 | 120 | 0.8054 |
| 0.6861 | 3.0 | 180 | 0.8149 |
| 0.9527 | 4.0 | 240 | 0.8036 |
| 0.5738 | 5.0 | 300 | 0.8096 |
Framework versions
- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
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