---
library_name: transformers
base_model: Qwen/Qwen3-8B
tags:
- generated_from_trainer
datasets:
- laion/CoderForge-Preview-v3-1000
model-index:
- name: e/data1/datasets/playground/ot-baf/checkpoints/cf-v3-1000-axolotl__Qwen3-8B
  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. -->

[<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: `0.16.0.dev0`
```yaml
# CoderForge v3 axolotl config template.
# Consumes the pre-tokenized laion/CoderForge-Preview-v3-<SIZE> datasets.
# Axolotl auto-detects pre-tokenized via input_ids + attention_mask + labels
# (_is_dataset_already_tokenized) and skips chat_template rendering entirely.
# Fill 1000 via sed-substitution.

base_model: Qwen/Qwen3-8B
deepspeed: /e/scratch/jureap59/feuer1/code/axolotl/deepspeed_configs/zero3_bf16.json

load_in_8bit: false
load_in_4bit: false

# plugins disabled 2026-04-22: CCE + bf16 + flash-attn on aarch64/torch2.9 caused
# gradient explosion (grad_norm 9.8e+11) and loss -> 0 within first 3-7 steps.
# plugins:
#   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin

# chat_template still set so tokenizer can be loaded, even though axolotl
# bypasses template rendering for pre-tokenized data.
chat_template: chatml
datasets:
  - path: laion/CoderForge-Preview-v3-1000
    # No `type:` specified — axolotl's _is_dataset_already_tokenized() fires
    # early and returns the dataset as-is.
    ds_type: parquet

dataset_prepared_path: /e/data1/datasets/playground/ot-baf/axolotl_dataset_cache/cf-v3-1000
output_dir: /e/data1/datasets/playground/ot-baf/checkpoints/cf-v3-1000-axolotl__Qwen3-8B
# hub_model_id: laion/CoderForge-Preview-v3-1000-axolotl__Qwen3-8B
# hub_strategy: end

# Upstream pre-tokenized sequences can exceed 80k tokens; matches Sera v3 truncation.
sequence_len: 32768

wandb_project:
wandb_entity:
wandb_watch:
wandb_name: cf-v3-1000-axolotl__Qwen3-8B
wandb_log_model:

# Matches upstream SERA config's optimization hparams for apples-to-apples.
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 1e-5
adam_beta1: 0.9
adam_beta2: 0.95

bf16: auto
tf32: false

gradient_checkpointing: true
activation_offloading: true
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_ratio: 0.1875
evals_per_epoch: 0
save_strategy: epoch

weight_decay: 0.01
max_grad_norm: 1.0
special_tokens:

```

</details><br>

# e/data1/datasets/playground/ot-baf/checkpoints/cf-v3-1000-axolotl__Qwen3-8B

This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) on the laion/CoderForge-Preview-v3-1000 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 31

### Training results



### Framework versions

- Transformers 5.5.0
- Pytorch 2.9.1+cu130
- Datasets 4.5.0
- Tokenizers 0.22.2