TinyLlama-1.1B-SlimOrca-Function-Calling-3T
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on the SlimOrca and glaive-function-calling-v2 datasets.
Evaluation
It achieves the following results on the evaluation set:
- Loss: 0.7403
Please see the scripts/llm-eval.py to recreate the evaluation results from the test split as published here: gardner/tinyllama-function-calling-eval. The model responds with function calling when expected and refuses when it doesn't have access to tools. In the linked dataset, result1 is generated by this model and result2 is from the test dataset.
axolotl version: 0.3.0
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: Open-Orca/SlimOrca-Dedup
type: sharegpt
conversation: chatml
- path: gardner/glaive-function-calling-v2-sharegpt
type: sharegpt
conversation: chatml
dataset_prepared_path: ./.prepared-datasets/glaive-function-calling-v2-sharegpt
val_set_size: 0.05
output_dir: ./tinyllama/function-calling/chatml
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
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: adamw_bnb_8bit
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: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
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: 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: 10
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2492 | 0.0 | 1 | 1.2363 |
| 0.7621 | 0.25 | 1896 | 0.8096 |
| 0.757 | 0.5 | 3792 | 0.7852 |
| 0.6424 | 0.75 | 5688 | 0.7717 |
| 0.5944 | 1.04 | 7584 | 0.7625 |
| 0.73 | 1.29 | 9480 | 0.7585 |
| 0.6781 | 1.54 | 11376 | 0.7521 |
| 0.829 | 1.79 | 13272 | 0.7471 |
| 0.6964 | 2.08 | 15168 | 0.7467 |
| 0.6652 | 2.33 | 17064 | 0.7453 |
| 0.7645 | 2.58 | 18960 | 0.7420 |
| 0.5702 | 2.83 | 20856 | 0.7392 |
| 0.7049 | 3.12 | 22752 | 0.7418 |
| 0.6087 | 3.37 | 24648 | 0.7412 |
| 0.6064 | 3.62 | 26544 | 0.7405 |
| 0.7125 | 3.87 | 28440 | 0.7403 |
Framework versions
- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0