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---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- generated_from_trainer
datasets:
- shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt
- shisa-ai/shisa-v2-roleplaying-sft
- shisa-ai/translation_set_april_6
- shisa-ai/rewild-set-deepseek-subset
- shisa-ai/magpie-ultra-set
- shisa-ai/magpie-advanced-questions-set
- shisa-ai/japan-magpie-set
- shisa-ai/shisa-v2-instruction-following-sft
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
model-index:
- name: outputs/ablation-191-finalsft2-shisa-v2-qwen2.5-7b
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: `0.8.0.dev0`
```yaml
# train w/ shisa-ai/shisa-v1-athenev2-reannotated-filtered
base_model: Qwen/Qwen2.5-7B-Instruct
load_in_8bit: false
load_in_4bit: false
strict: false
# User Liger
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true
datasets:
- path: shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
- path: shisa-ai/shisa-v2-roleplaying-sft
type: chat_template
field_messages: conversations
message_property_mappings:
role: role
content: content
roles:
system:
- system
assistant:
- gpt
- model
- assistant
user:
- human
- user
roles_to_train: ["assistant"]
- path: shisa-ai/translation_set_april_6
split: train[:25%]
type: chat_template
field_messages: conversations
message_property_mappings:
role: role
content: content
roles:
system:
- system
assistant:
- gpt
- model
- assistant
user:
- human
- user
roles_to_train: ["assistant"]
- path: shisa-ai/rewild-set-deepseek-subset
split: train[:25%]
type: chat_template
field_messages: conversations
message_property_mappings:
role: role
content: content
roles:
system:
- system
assistant:
- gpt
- model
- assistant
user:
- human
- user
roles_to_train: ["assistant"]
- path: shisa-ai/magpie-ultra-set
split: train[:8%]
type: chat_template
field_messages: conversations
message_property_mappings:
role: role
content: content
roles:
system:
- system
assistant:
- gpt
- model
- assistant
user:
- human
- user
roles_to_train: ["assistant"]
- path: shisa-ai/magpie-advanced-questions-set
split: train[:8%]
type: chat_template
field_messages: conversations
message_property_mappings:
role: role
content: content
roles:
system:
- system
assistant:
- gpt
- model
- assistant
user:
- human
- user
roles_to_train: ["assistant"]
- path: shisa-ai/japan-magpie-set
split: train
type: chat_template
field_messages: conversations
message_property_mappings:
role: role
content: content
roles:
system:
- system
assistant:
- gpt
- model
- assistant
user:
- human
- user
roles_to_train: ["assistant"]
- path: shisa-ai/shisa-v2-instruction-following-sft
split: train[:50%]
type: chat_template
field_messages: conversations
message_property_mappings:
role: role
content: content
roles:
system:
- system
assistant:
- gpt
- model
- assistant
user:
- human
- user
roles_to_train: ["assistant"]
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/ablation-191-finalsft2-shisa-v2-qwen2.5-7b
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
# marginal difference
neftune_noise_alpha: 5
use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: ablation-191-finalsft2-shisa-v2-qwen2.5-7b
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 0
save_total_limit: 1 # Only store a single checkpoint
debug:
deepspeed: zero3_bf16.json
weight_decay: 1e-4
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# outputs/ablation-191-finalsft2-shisa-v2-qwen2.5-7b
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the shisa-ai/shisa-v2-best-of-n-athenev2-tulu70b-llama33-only-no-sysprompt, the shisa-ai/shisa-v2-roleplaying-sft, the shisa-ai/translation_set_april_6, the shisa-ai/rewild-set-deepseek-subset, the shisa-ai/magpie-ultra-set, the shisa-ai/magpie-advanced-questions-set, the shisa-ai/japan-magpie-set and the shisa-ai/shisa-v2-instruction-following-sft datasets.
It achieves the following results on the evaluation set:
- Loss: 0.7119
## 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
- distributed_type: multi-GPU
- num_devices: 32
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0667 | 0.0026 | 1 | 1.0905 |
| 0.7574 | 0.5 | 189 | 0.7749 |
| 0.7322 | 1.0 | 378 | 0.7368 |
| 0.6711 | 1.5 | 567 | 0.7233 |
| 0.6645 | 2.0 | 756 | 0.7122 |
| 0.6144 | 2.5 | 945 | 0.7146 |
| 0.6031 | 3.0 | 1134 | 0.7119 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
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