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: