---
library_name: peft
license: other
base_model: minpeter/HCX-SEED-FC-3B
tags:
- axolotl
- generated_from_trainer
datasets:
- minpeter/xlam-function-calling-60k-hermes
- minpeter/xlam-irrelevance-7.5k-qwen2.5-72b-distill-hermes
- minpeter/hermes-function-calling-v1-jsonl
- minpeter/hermes-function-calling-v1-jsonl
model-index:
- name: LoRA-HCX-3b-sf-xlam-00
  results: []
---

## 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                        |

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.10.0.dev0`
```yaml
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|>"

```

</details><br>

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

This model is a fine-tuned version of [minpeter/HCX-SEED-FC-3B](https://huggingface.co/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