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---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-14B
model-index:
- name: LLaMutation-Qwen2.5-14B-SFFT-v0.0
results: []
---
# LLaMutation-Qwen2.5-14B-SFFT-v0.0
This model is a [Spectrum](https://github.com/axolotl-ai-cloud/axolotl/blob/67f744dc8c9564ef7a42d5df780ae53e319dca61/src/axolotl/integrations/spectrum/README.md) FFT of [Qwen/Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) on a code translation dataset evolved with [EvolKit](https://github.com/arcee-ai/EvolKit).
## Model description
Code translation and completion model trained on Qwen2.5-14B as there is not yet a Qwen2.5-Coder-14B model. This is 100% an alpha completion model thus there will be quirks to it's useage parameters.
I will refine the model both for completion and create an instruct/chat variant.
## Intended uses & limitations
Differing system prompts for code translation and use as a tab autocomplete model with [continue.dev](https://www.continue.dev/)
## Chat template and sampling paramaters.
Chat template is chatml.
Sampling parameters for the generation and demo at the hackathon are here:
### SYSTEM PROMPT MUST BE USED FOR THIS MODEL
`You are an Al assistant that is an expert at converting code from any language to another within properly formatted code blocks. DON'T SAY ANYTHING ABOUT NOT SEEING CODE. Keep non code text to the a minimum possible. DO NOT REPEAT ANY NON CODE TEXT. ONLY PRINT OUT CODE ONCE DO NOT ITTERATE!`
## Training procedure
Spectrum FFT/SFFT
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3948 | 0.0237 | 1 | 0.3920 |
| 0.2392 | 0.4970 | 21 | 0.2500 |
| 0.2606 | 0.9941 | 42 | 0.2621 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
[<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: `0.4.1`
```yaml
base_model: Qwen/Qwen2.5-14B
load_in_8bit: false
load_in_4bit: false
strict: false
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
plugins:
- axolotl.integrations.spectrum.SpectrumPlugin
spectrum_top_fraction: 0.5
# Optional if using a pre-scanned model as your base_model. Useful if using a model mirror
spectrum_model_name: Qwen/Qwen2.5-14B
datasets:
- path: datasets/LLaMutation.jsonl
type: sharegpt
- path: datasets/LLaMutationMAX_Train.json
type: sharegpt
chat_template: chatml
shuffle_merged_datasets: true
val_set_size: 0.1
output_dir: ./LLaMutation-Qwen2.5-14B-SFFT-v0.0
sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
# adapter: qlora
# lora_model_dir:
# lora_r: 32
# lora_alpha: 16
# lora_dropout: 0.05
# lora_target_linear: true
# peft_use_dora: true
wandb_project: LLaMutation-Qwen2.5-14B-SFFT-v0.0
wandb_entity:
wandb_watch:
wandb_name: Unit-00
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: linear
learning_rate: 0.0005
max_grad_norm: 3
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 50
evals_per_epoch: 2
saves_per_epoch: 2
save_safetensors: true
hub_model_id:
hub_strategy:
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.1
# fsdp:
# - full_shard
# - auto_wrap
# fsdp_config:
# fsdp_limit_all_gathers: true
# fsdp_sync_module_states: true
# fsdp_offload_params: false # Changed from true
# fsdp_use_orig_params: true # Changed from false
# fsdp_cpu_ram_efficient_loading: true
# fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
# fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer
# fsdp_activation_checkpointing: true
# fsdp_state_dict_type: SHARDED_STATE_DICT # Changed from FULL_STATE_DICT
# fsdp_sharding_strategy: FULL_SHARD
# fsdp_forward_prefetch: true # Added
# fsdp_backward_prefetch: "BACKWARD_POST" # Added
# fsdp_backward_prefetch_limit: 1 # Added
# fsdp_mixed_precision: BF16 # Added
```
</details><br> |