metadata
base_model:
- concedo/KobbleTinyV2-1.1B
library_name: transformers
tags:
- mergekit
- merge
Tinyllama-2B
This is a merge and a finetune to create a small, but very useable Model, and i have to say, its very good.
Try this Model in GGUF Q8 on my homepage here
Basic Question:
Prompt Template
Tinyllama-2B uses Alpaca:
### Instruction:
{prompt}
### Response:
Merge Info:
This is a frankenmerge of: concedo/KobbleTinyV2-1.1B
The following YAML configuration was used to produce this model:
dtype: bfloat16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 16]
model: concedo/KobbleTinyV2-1.1B
- sources:
- layer_range: [5, 16]
model: concedo/KobbleTinyV2-1.1B
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [5, 16]
model: concedo/KobbleTinyV2-1.1B
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [16, 22]
model: concedo/KobbleTinyV2-1.1B
Finetune Info:
The following YAML configuration was used to finetune this model:
base_model: Fischerboot/2b-tiny-llama-alpaca-instr
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: Fischerboot/freedom-rp-alpaca-shortend
type: alpaca
- path: diffnamehard/toxic-dpo-v0.1-NoWarning-alpaca
type: alpaca
- path: Fischerboot/alpaca-undensored-fixed-50k
type: alpaca
- path: Fischerboot/DAN-alpaca
type: alpaca
- path: Fischerboot/rp-alpaca-next-oone
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/24r
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: 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:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention: true
flash_attention: true
warmup_steps: 10
evals_per_epoch: 2
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
Training results:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7881 | 0.0017 | 1 | 2.5329 |
| 1.6899 | 0.4996 | 287 | 1.9272 |
| 1.5511 | 0.9991 | 574 | 1.8750 |
| 1.4797 | 1.4861 | 861 | 1.8476 |
| 1.5279 | 1.9856 | 1148 | 1.8270 |
| 1.4583 | 2.4726 | 1435 | 1.8275 |
| 1.5044 | 2.9721 | 1722 | 1.8215 |
| 1.3051 | 3.4582 | 2009 | 1.8243 |
| 1.5619 | 3.9578 | 2296 | 1.8245 |