---
base_model: mayflowergmbh/occiglot-10b-de-en-instruct
tags:
- generated_from_trainer
model-index:
- name: occiglot10b/
results: []
---
[](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config
axolotl version: `0.4.0`
```yaml
base_model: mayflowergmbh/occiglot-10b-de-en-instruct
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: true
strict: false
rl: dpo
datasets:
- path: johannhartmann/mistralorpo
split: train
type: chatml.intel
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./occiglot10b/
save_total_limit: 3
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: false
pad_to_sequence_len: false
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_modules_to_save:
- embed_tokens
- lm_head
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project: occiglot
wandb_entity: mayflowerteam
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
adam_beta2: 0.95
adam_epsilion: 0.00001
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_steps:
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 239
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
save_safetensors: true
special_tokens:
eos_token: "<|im_end|>"
tokens: # these are delimiters
- "<|im_start|>"
chat_template: chatml
```
# occiglot10b/
This model is a fine-tuned version of [mayflowergmbh/occiglot-10b-de-en-instruct](https://huggingface.co./mayflowergmbh/occiglot-10b-de-en-instruct) on an unknown 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 2698
### Training results
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0