---
library_name: transformers
license: other
base_model: LiquidAI/LFM2-1.2B
tags:
- generated_from_trainer
model-index:
- name: outputs/lfm2-sft-reasoning-2
  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. -->


To enable reasoning include in your system prompt "/thinking_on"
To disable reasoning include in your system prompt "/thinking_off"


[<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: `0.8.0`
```yaml
base_model: LiquidAI/LFM2-1.2B
flash_attention: true
sample_packing: true
chunked_cross_entropy: true
learning_rate: 1e-4
sequence_len: 16384
micro_batch_size: 2
gradient_accumulation_steps: 4
gradient_checkpointing: true
optimizer: adamw_torch_8bit
lr_scheduler: cosine
warmup_ratio: 0.2
float16: true
bf16: true
max_grad_norm: 0.1
#num_epochs: 3
max_steps: 3000
saves_per_epoch: 1
logging_steps: 5
output_dir: ./outputs/lfm2-sft-reasoning-2
chat_template: tokenizer_default
datasets:
  # - path: winglian/pirate-ultrachat-10k
  #   type: chat_template
  #   split: train
  #   datasets:
  # - path: interstellarninja/hermes_reasoning_tool_use
  #   type: chat_template
  #   field_messages: conversations
  #   message_property_mappings: 
  #     role : from
  #     content : value
      
  - path: ./nemotron2
    type: chat_template
    field_messages: messages
    message_property_mappings: 
      role : role
      content : content
eot_tokens:
  - "<|im_end|>"

tokens:
  - "<think>"
  - "</think>"
  - "<tool_call>"
  - "</tool_call>"
  - "<tool_response>"
  - "</tool_response>"
# dataloader_prefetch_factor: 8
# dataloader_num_workers: 8
# dataloader_pin_memory: true

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name: LiquidAI-reasoning-sft-2
wandb_log_model:

val_set_size: 0.1
evals_per_epoch: 4
#eval_max_new_tokens: 128
```

</details><br>

# outputs/lfm2-sft-reasoning-2

This model is a fine-tuned version of [LiquidAI/LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8586

## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch_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: 317
- training_steps: 1589

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0006 | 1    | 2.1367          |
| 0.8178        | 0.2504 | 398  | 0.8964          |
| 0.7304        | 0.5007 | 796  | 0.8835          |
| 0.6795        | 0.7511 | 1194 | 0.8586          |


### Framework versions

- Transformers 4.54.0
- Pytorch 2.7.1+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1