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---
library_name: transformers
base_model: jeiku/Magic_8B
tags:
- generated_from_trainer
model-index:
- name: outputs/out
results: []
---
[](https://hf.co/QuantFactory)
# QuantFactory/Fatgirl_8B-GGUF
This is quantized version of [jeiku/Fatgirl_8B](https://huggingface.co/jeiku/Fatgirl_8B) created using llama.cpp
# Original Model Card
<!-- 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: `0.4.1`
```yaml
base_model: jeiku/Magic_8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: anthracite-org/stheno-filtered-v1.1
type: sharegpt
conversation: chatml
- path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
type: sharegpt
conversation: chatml
- path: ResplendentAI/bluemoon
type: sharegpt
conversation: chatml
- path: openerotica/freedom-rp
type: sharegpt
conversation: chatml
- path: MinervaAI/Aesir-Preview
type: sharegpt
conversation: chatml
chat_template: chatml
val_set_size: 0.01
output_dir: ./outputs/out
adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
sequence_len: 8192
# sequence_len: 32768
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
wandb_project: New8B
wandb_entity:
wandb_watch:
wandb_name: New8B
wandb_log_model:
gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00001
weight_decay: 0.05
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 2
debug:
deepspeed:
fsdp:
fsdp_config:
special_tokens:
pad_token: <pad>
```
</details><br>
# outputs/out
This model is a fine-tuned version of [jeiku/Magic_8B](https://huggingface.co/jeiku/Magic_8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3029
## 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: 2
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 32
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.447 | 0.0062 | 1 | 1.4349 |
| 1.3437 | 0.2530 | 41 | 1.3502 |
| 1.3734 | 0.5060 | 82 | 1.3237 |
| 1.3543 | 0.7590 | 123 | 1.3128 |
| 1.319 | 1.0102 | 164 | 1.3064 |
| 1.2886 | 1.2636 | 205 | 1.3042 |
| 1.2387 | 1.5169 | 246 | 1.3031 |
| 1.3746 | 1.7702 | 287 | 1.3029 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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