Comparison Table of Test Results by Model (BFCL)

Test Item	base Model Score	tool Model Score	Score Difference (tool - base)
irrelevance	0.4333	0.8708	+0.4375
multi_turn_base	0.0100	0.0400	+0.0300
parallel_multiple	0.4750	0.7550	+0.2800
parallel	0.5200	0.7600	+0.2400
simple	0.7575	0.7975	+0.0400
multiple	0.7650	0.8050	+0.0400

See axolotl config

axolotl version: 0.10.0.dev0

base_model: minpeter/HCX-SEED-FC-3B
hub_model_id: minpeter/LoRA-HCX-3b-sf-xlam-00

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
    roles_to_train: ["assistant"]
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    shards: 120
  - path: minpeter/xlam-irrelevance-7.5k-qwen2.5-72b-distill-hermes
    data_files:
      - result.parquet
    type: chat_template
    roles_to_train: ["assistant"]
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    shards: 15
  - path: minpeter/hermes-function-calling-v1-jsonl
    data_files:
      - func-calling-singleturn.jsonl
      - func-calling.jsonl
    type: chat_template
    roles_to_train: ["assistant"]
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    shards: 3
  - path: minpeter/hermes-function-calling-v1-jsonl
    data_files:
      - glaive-function-calling-5k.jsonl
    type: chat_template
    roles_to_train: ["assistant"]
    field_messages: conversations
    message_property_mappings:
      role: from
      content: value
    shards: 5
chat_template: chatml

dataset_prepared_path: last_run_prepared

output_dir: ./output

adapter: lora
lora_model_dir:

sequence_len: 20000
pad_to_sequence_len: true
sample_packing: true

val_set_size: 0.05
eval_sample_packing: true
evals_per_epoch: 3

lora_r: 8
lora_alpha: 16
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: 2
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:

special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|endoftext|>"

LoRA-HCX-3b-sf-xlam-00

This model is a fine-tuned version of minpeter/HCX-SEED-FC-3B on the minpeter/xlam-function-calling-60k-hermes, the minpeter/xlam-irrelevance-7.5k-qwen2.5-72b-distill-hermes, the minpeter/hermes-function-calling-v1-jsonl and the minpeter/hermes-function-calling-v1-jsonl datasets. It achieves the following results on the evaluation set:

• Loss: 0.3603

Model description

More information needed

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: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.8224	0.0215	1	0.7483
0.4965	0.3441	16	0.4406
0.5057	0.6882	32	0.3911
0.4464	1.0215	48	0.3737
0.4376	1.3656	64	0.3651
0.3833	1.7097	80	0.3603

Framework versions

• PEFT 0.15.2
• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.1
• Tokenizers 0.21.1