Modelpublic

xiaolesu/OsmosisProofling-SFT-NT-GRPO-TK-V2

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

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Model Card

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

axolotl version: 0.16.0.dev0

yaml

base_model: Qwen/Qwen3-8B

load_in_8bit: false
load_in_4bit: false
strict: false

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

chat_template: qwen3

chat_template_kwargs:
  enable_thinking: false

datasets:
  - path: xiaolesu/OsmosisProofling-SFT
    type: alpaca
    split: train

test_datasets:
  - path: xiaolesu/OsmosisProofling-SFT
    type: alpaca
    split: validation

output_dir: ./outputs/OsmosisProofling-SFT/

sequence_len: 4096
sample_packing: true
flex_attention: true

flex_attn_compile_kwargs:
  dynamic: false
  mode: max-autotune-no-cudagraphs

wandb_project: OsmosisProofling-SFT
wandb_entity:
wandb_watch:
wandb_name: OsmosisProofling-SFT-Run1
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 1e-5

bf16: true
tf32: true

resume_from_checkpoint:
logging_steps: 5

evals_per_epoch: 10
saves_per_epoch: 10
save_total_limit: 3

warmup_ratio: 0.1
weight_decay: 0.0
fsdp:
  - full_shard
  - auto_wrap

fsdp_config:
  fsdp_version: 2
  fsdp_offload_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: Qwen3DecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD
  fsdp_reshard_after_forward: true
  fsdp_activation_checkpointing: true

special_tokens:

</details><br>

outputs/OsmosisProofling-SFT/

This model is a fine-tuned version of Qwen/Qwen3-8B on the xiaolesu/OsmosisProofling-SFT dataset. It achieves the following results on the evaluation set:

—Loss: 0.3543
—Ppl: 1.4252
—Memory/max Active (gib): 20.98
—Memory/max Allocated (gib): 20.98
—Memory/device Reserved (gib): 36.0

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: 1e-05
—trainbatchsize: 2
—evalbatchsize: 2
—seed: 42
—distributed_type: multi-GPU
—num_devices: 7
—totaltrainbatch_size: 14
—totalevalbatch_size: 14
—optimizer: Use OptimizerNames.ADAMWTORCHFUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
—lrschedulertype: cosine
—lrschedulerwarmup_steps: 21
—training_steps: 212

Training results

Training Loss	Epoch	Step	Validation Loss	Ppl	Active (gib)	Allocated (gib)	Reserved (gib)
No log	0	0	1.3417	3.8257	16.56	16.56	20.27
1.2425	0.1048	11	0.9643	2.6231	20.98	20.98	36.1
0.7372	0.2095	22	0.5572	1.7458	20.98	20.98	36.0
0.5042	0.3143	33	0.4529	1.5728	20.98	20.98	36.0
0.4350	0.4190	44	0.4158	1.5155	20.98	20.98	36.0
0.3719	0.5238	55	0.3908	1.4782	20.98	20.98	36.0
0.3934	0.6286	66	0.3780	1.4594	20.98	20.98	36.0
0.3594	0.7333	77	0.3696	1.4471	20.98	20.98	36.0
0.3513	0.8381	88	0.3645	1.4398	20.98	20.98	36.0
0.3499	0.9429	99	0.3616	1.4356	20.98	20.98	36.0
0.3517	1.0476	110	0.3583	1.4309	20.98	20.98	36.0
0.3422	1.1524	121	0.3567	1.4286	20.98	20.98	36.0
0.3219	1.2571	132	0.3557	1.4272	20.98	20.98	36.0
0.3098	1.3619	143	0.3552	1.4264	20.98	20.98	36.0
0.3068	1.4667	154	0.3546	1.4257	20.98	20.98	36.0
0.3168	1.5714	165	0.3545	1.4254	20.98	20.98	36.0
0.3198	1.6762	176	0.3546	1.4256	20.98	20.98	36.0
0.3207	1.7810	187	0.3544	1.4253	20.98	20.98	36.0
0.3232	1.8857	198	0.3541	1.4249	20.98	20.98	36.0
0.3441	1.9905	209	0.3543	1.4252	20.98	20.98	36.0

Framework versions

—Transformers 5.3.0
—Pytorch 2.9.1+cu128
—Datasets 4.5.0
—Tokenizers 0.22.2