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Model: laion/CoderForge-Preview-v3-316-axolotl__Qwen3-8B Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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base_model: Qwen/Qwen3-8B
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tags:
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- generated_from_trainer
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datasets:
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- laion/CoderForge-Preview-v3-316
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model-index:
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- name: e/data1/datasets/playground/ot-baf/checkpoints/cf-v3-316-axolotl__Qwen3-8B
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<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)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.16.0.dev0`
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```yaml
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# CoderForge v3 axolotl config template.
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# Consumes the pre-tokenized laion/CoderForge-Preview-v3-<SIZE> datasets.
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# Axolotl auto-detects pre-tokenized via input_ids + attention_mask + labels
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# (_is_dataset_already_tokenized) and skips chat_template rendering entirely.
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# Fill 316 via sed-substitution.
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base_model: Qwen/Qwen3-8B
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deepspeed: /e/scratch/jureap59/feuer1/code/axolotl/deepspeed_configs/zero3_bf16.json
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load_in_8bit: false
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load_in_4bit: false
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# plugins disabled 2026-04-22: CCE + bf16 + flash-attn on aarch64/torch2.9 caused
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# gradient explosion (grad_norm 9.8e+11) and loss -> 0 within first 3-7 steps.
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# plugins:
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# - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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# chat_template still set so tokenizer can be loaded, even though axolotl
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# bypasses template rendering for pre-tokenized data.
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chat_template: chatml
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datasets:
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- path: laion/CoderForge-Preview-v3-316
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# No `type:` specified — axolotl's _is_dataset_already_tokenized() fires
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# early and returns the dataset as-is.
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ds_type: parquet
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dataset_prepared_path: /e/data1/datasets/playground/ot-baf/axolotl_dataset_cache/cf-v3-316
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output_dir: /e/data1/datasets/playground/ot-baf/checkpoints/cf-v3-316-axolotl__Qwen3-8B
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# hub_model_id: laion/CoderForge-Preview-v3-316-axolotl__Qwen3-8B
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# hub_strategy: end
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# Upstream pre-tokenized sequences can exceed 80k tokens; matches Sera v3 truncation.
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sequence_len: 32768
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wandb_project:
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wandb_entity:
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wandb_watch:
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wandb_name: cf-v3-316-axolotl__Qwen3-8B
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wandb_log_model:
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# Matches upstream SERA config's optimization hparams for apples-to-apples.
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gradient_accumulation_steps: 8
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micro_batch_size: 1
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num_epochs: 3
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optimizer: adamw_torch
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lr_scheduler: cosine
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learning_rate: 1e-5
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adam_beta1: 0.9
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adam_beta2: 0.95
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bf16: auto
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tf32: false
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gradient_checkpointing: true
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activation_offloading: true
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resume_from_checkpoint:
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logging_steps: 1
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flash_attention: true
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loss_watchdog_threshold: 5.0
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loss_watchdog_patience: 3
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warmup_ratio: 0.1875
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evals_per_epoch: 0
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save_strategy: epoch
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weight_decay: 0.01
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max_grad_norm: 1.0
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special_tokens:
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```
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</details><br>
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# e/data1/datasets/playground/ot-baf/checkpoints/cf-v3-316-axolotl__Qwen3-8B
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This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the laion/CoderForge-Preview-v3-316 dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- total_eval_batch_size: 4
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 2
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- training_steps: 9
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### Training results
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### Framework versions
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- Transformers 5.5.0
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- Pytorch 2.9.1+cu130
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- Datasets 4.5.0
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- Tokenizers 0.22.2
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