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fused-v0

Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
fused-v0
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---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- generated_from_trainer
model-index:
- name: outputs/qlora-out
  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: `0.6.0`
```yaml
base_model: Qwen/Qwen2.5-Coder-7B-Instruct

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

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  # geopandas
  - path: https://www.fused.io/server/v1/realtime-shared/fsh_7UePa8c68x8u89FjmK2Tuu/run/file?dtype_out_vector=parquet
    type: pretrain
    ds_type: parquet
    text_column: text
    split: train
  # examples
  - path: https://staging.fused.io/server/v1/realtime-shared/fsh_2xCVySNfnwmUhWPssX24cn/run/file?dtype_out_raster=png&dtype_out_vector=parquet&cb=12345
    type: pretrain
    ds_type: parquet
    text_column: text
    split: train
  # docs
  - path: https://www.fused.io/server/v1/realtime-shared/fsh_EycsvX70Y3WosxHhdJ8Y9/run/file?dtype_out_raster=png&dtype_out_vector=parquet
    type: pretrain
    ds_type: parquet
    text_column: text
    split: train
  - path: mlabonne/FineTome-100k
    type: chat_template
    split: train[:1%]
    chat_template: qwen_25
    field_messages: conversations
    message_field_role: from
    message_field_content: value

dataset_prepared_path: last_run_prepared
val_set_size: 0.
output_dir: ./outputs/qlora-out

wandb_project: fused-io-copilot
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:

sequence_len: 8192
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false


gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 2
optimizer: lion_8bit
lr_scheduler: cosine
learning_rate: 0.00001

train_on_inputs: false
group_by_length: false
bf16: true
fp16:
tf32: true

gradient_checkpointing: true
logging_steps: 1
flash_attention: true

warmup_steps: 20
saves_per_epoch: 1
deepspeed:
weight_decay: 0.01
special_tokens:
  pad_token: "<|end_of_text|>"

save_safetensors: true

```

</details><br>

# outputs/qlora-out

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 https://www.fused.io/server/v1/realtime-shared/fsh_7UePa8c68x8u89FjmK2Tuu/run/file?dtype_out_vector=parquet, the https://staging.fused.io/server/v1/realtime-shared/fsh_2xCVySNfnwmUhWPssX24cn/run/file?dtype_out_raster=png&dtype_out_vector=parquet&cb=12345, the https://www.fused.io/server/v1/realtime-shared/fsh_EycsvX70Y3WosxHhdJ8Y9/run/file?dtype_out_raster=png&dtype_out_vector=parquet and the mlabonne/FineTome-100k datasets.

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.LION_8BIT and the args are:
No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 2

### Training results



### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0