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fr-lora
shenghuanjiuer/fr-lora
fr-lora is a machine learning model from shenghuanjiuer. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- apache-2.0
- Model type
- qwen2
- Access
- Public
- Created
- Jan 4, 2026
- Updated
- Jan 4, 2026
- SHA
- 1ce11760
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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. -->
<details><summary>See axolotl config</summary>
axolotl version: 0.11.0.dev0
adapter: lora
base_model: Qwen/Qwen2.5-7B-Instruct
bf16: false
chat_template: chatml
dataset_prepared_path: last_run_prepared
datasets:
- field_messages: messages
path: shenghuanjiuer/sft-train
type: chat_template
eval_sample_packing: true
evals_per_epoch: 4
flash_attention: true
fp16: true
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
hub_model_id: shenghuanjiuer/fr-lora
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_r: 16
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
loss_watchdog_patience: 3
loss_watchdog_threshold: 5
lr_scheduler: cosine
micro_batch_size: 1
num_epochs: 6
optimizer: adamw_8bit
output_dir: /runpod-volume/fine-tuning/test-run
pad_to_sequence_len: true
push_to_hub: true
run_name: test-run
runpod_job_id: da89ac2d-7df2-469f-819c-e5dca7a3ecb7-e2
sample_packing: true
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
train_on_inputs: false
val_set_size: 0.1
warmup_steps: 10
weight_decay: 0
fr-lora
This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the shenghuanjiuer/sft-train dataset. It achieves the following results on the evaluation set:
- Loss: 5.6124
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- 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
- training_steps: 84
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0 | 0 | 7.8256 |
| 7.8065 | 0.2857 | 4 | 5.6124 |
Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Library
- peft
- Type
- qwen2
- License
- apache-2.0
- Created
- Jan 4, 2026
- Updated
- Jan 4, 2026