Qwen3.5-27B-earica-Derestricted-Lite

Creative model

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Hourly Usage

Performance Metrics

Avg. Total Time

N/A

Avg. TTFT

N/A

Avg. Prefill TPS

N/A

Avg. Gen TPS

N/A

Model Information

Context Size

262144

Quantization

r64

Engine

vllm

Creation Method

LoRA

Model Type

Qwen35

Chat Template

Qwen3.5

Reasoning

Yes

Vision

Yes

Parameters

27B

Added At

4/27/2026

library_name: transformers license: apache-2.0 base_model: Qwen/Qwen3.5-27B tags:

  • axolotl
  • generated_from_trainer datasets:
  • voidful/earica_text_train_v2 model-index:
  • name: Qwen3.5-27B-earica results: []

Built with Axolotl

See axolotl config

axolotl version: 0.16.0.dev0

base_model: Qwen/Qwen3.5-27B
low_cpu_mem_usage: true

plugins:
  - axolotl.integrations.drift.DriftPlugin
  - axolotl.integrations.liger.LigerPlugin

liger_rms_norm: true
liger_glu_activation: true

drift_trainer: true
drift_rho: 0.999
drift_beta: 0.5
drift_tau: 1.0
drift_lambda: 1.0

datasets:
  - path: voidful/earica_text_train_v2
    type: chat_template
    field_messages: conversations
    split: train
    split_thinking: true

dataset_prepared_path: ./prepared_data/drift_27b


chat_template: qwen3_5

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: false

gradient_accumulation_steps: 2
micro_batch_size: 1
batch_flattening: false
group_by_length: true
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-5

bf16: true
gradient_checkpointing: true
flash_attention: true
dataloader_num_workers: 4
val_set_size: 0.05
save_strategy: epoch
output_dir: ./outputs/drift-27b

deepspeed: deepspeed_configs/zero2.json

hub_model_id: voidful/Qwen3.5-27B-earica
push_to_hub: true
hub_strategy: end

log_on_each_node: false
logging_steps: 1

Qwen3.5-27B-earica

This model is a fine-tuned version of Qwen/Qwen3.5-27B on the voidful/earica_text_train_v2 dataset.

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: 2e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 160
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 320
  • total_eval_batch_size: 160
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 17
  • training_steps: 576

Training results

Framework versions

  • Transformers 5.3.0
  • Pytorch 2.8.0+cu128
  • Datasets 4.5.0
  • Tokenizers 0.22.2