base_model: Qwen/Qwen2.5-7B-Instruct
hub_model_id: morsmordre/q-7b-v1-iteration-00-sf-xlam-04

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: minpeter/xlam-function-calling-60k-hermes
    data_files:
      - result.parquet
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    shards: 30
  - path: minpeter/xlam-irrelevance-7.5k-qwen2.5-72b-distill-hermes
    data_files:
      - result.parquet
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    shards: 6
  - path: minpeter/bfcl-v1-non-live-ast-hermes
    data_files:
      - result.parquet
    type: chat_template
    chat_template: chatml
    field_messages: conversations
    message_field_role: from
    message_field_content: value

chat_template: chatml

dataset_prepared_path: last_run_prepared

output_dir: ./output

adapter: lora
lora_model_dir:

sequence_len: 4096
pad_to_sequence_len: true
sample_packing: true

val_set_size: 0.05
eval_sample_packing: true
evals_per_epoch: 3

lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project: "axolotl"
wandb_entity: "kasfiekfs-e"
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_8bit
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:
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:

q-7b-v1-iteration-00-sf-xlam-04

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the minpeter/xlam-function-calling-60k-hermes, the minpeter/xlam-irrelevance-7.5k-qwen2.5-72b-distill-hermes and the minpeter/bfcl-v1-non-live-ast-hermes datasets. It achieves the following results on the evaluation set:

Loss: 0.1309

Model description

Test Type	Adapter	Base Model	Improvement
irrelevance	0.850	0.642	+0.208
parallel_multiple	0.930	0.855	+0.075
parallel	0.940	0.900	+0.040
simple	0.965	0.953	+0.012
multiple	0.965	0.950	+0.015

This adapter shows significant improvements across all test categories compared to the base model, with the most notable enhancement in irrelevance detection (+20.8%). The improvements in other categories range from 1.2% to 7.5%, demonstrating consistent performance gains.

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

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: 2
total_train_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.6485	0.0058	1	0.4210
0.1926	0.3324	57	0.1426
0.1245	0.6647	114	0.1328
0.0495	0.9971	171	0.1309

Framework versions

PEFT 0.14.0
Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

minpeter/LoRA-Qwen-7b-v1-iteration-00-sf-xlam-04