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wizl_base_7b-fsv/README.md
ModelHub XC 44566d27e7 初始化项目,由ModelHub XC社区提供模型
Model: jadechoi/wizl_base_7b-fsv
Source: Original Platform
2026-06-13 16:20:58 +08:00

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---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- axolotl
- generated_from_trainer
datasets:
- train.jsonl
model-index:
- name: wizl_base_7b-fsv
results: []
---
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<details><summary>See axolotl config</summary>
axolotl version: `0.12.2`
```yaml
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
load_in_8bit: false
load_in_4bit: false
datasets:
- path: train.jsonl
type: chat_template
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./outputs/out
adapter:
lora_model_dir:
sequence_len: 5120
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
wandb_project: wizl-base-m
wandb_entity:
wandb_watch:
wandb_name: 8b-base-fsv
wandb_log_model:
hub_model_id: jadechoi/wizl_base_7b-fsv
gradient_accumulation_steps: 4
micro_batch_size: 16
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 2e-5
bf16: true
fp16:
tf32: false
gradient_checkpointing:
logging_steps: 1
flash_attention: true
eager_attention:
warmup_ratio: 0.05
evals_per_epoch: 0
saves_per_epoch: 1
weight_decay: 0.01
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
fsdp_activation_checkpointing: true
# save_first_step: true # uncomment this to validate checkpoint saving works with your config
```
</details><br>
# wizl_base_7b-fsv
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the train.jsonl 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: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- 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: 11
- training_steps: 220
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4