README.md

---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- axolotl
- generated_from_trainer
datasets:
- minpeter/xlam-function-calling-60k-hermes
- minpeter/xlam-irrelevance-7.5k-qwen2.5-72b-distill-hermes
- minpeter/bfcl-v1-non-live-ast-hermes
model-index:
- name: m-3b-v1-iteration-00-sf-xlam-06
  results: []
---

<!-- 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: `0.7.0`
```yaml
base_model: meta-llama/Llama-3.2-3B-Instruct
hub_model_id: morsmordre/m-3b-v1-iteration-00-sf-xlam-06

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: llama3
    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: llama3
    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: llama3
    field_messages: conversations
    message_field_role: from
    message_field_content: value

chat_template: llama3

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: 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: 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:

special_tokens:
  pad_token: "<|finetune_right_pad_id|>"
```

</details><br>

# m-3b-v1-iteration-00-sf-xlam-06

This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-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: 0.1362

## Model description

| Test Type (bfcl)  | Base Model Accuracy | This Adapter Accuracy | Improvement |
| ----------------- | ------------------- | --------------------- | ----------- |
| irrelevance       | 72.08               | 74.17                 | +2.09       |
| parallel_multiple | 10.00               | 90.00                 | +80.00      |
| parallel          | 11.50               | 92.00                 | +80.50      |
| simple            | 24.75               | 95.00                 | +70.25      |
| multiple          | 20.00               | 93.50                 | +73.50      |


## Inference

```sh
vllm serve meta-llama/Llama-3.2-3B-Instruct \
--enable-auto-tool-choice --tool-call-parser llama_hermes --tool-parser-plugin github.com/minpeter/hermes-llama-parse/lh_tool_parser.py  \
--port 4000 --enable-lora --lora-modules tool='morsmordre/m-3b-v1-iteration-00-sf-xlam-06'
```

*The hermes parser of the existing vllm expects the text <tool_call> and </tool_call> to exist as a single token in the vocab, so a modified "llama_hermes" parser is required. As of v0.7.3, you can inject the plugin using the --tool-parser-plugin flag.*

## Model Evaluation Results

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: 1.0

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
| :-----------: | :----: | :---: | :-------------: |
|    0.6162     | 0.0059 |   1   |     0.4366      |
|    0.2323     | 0.3343 |  57   |     0.1475      |
|    0.1065     | 0.6686 |  114  |     0.1362      |


### Framework versions

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