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---
base_model: meta-llama/Meta-Llama-3-8B
library_name: peft
license: llama3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Sanskrit-llama
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: `0.4.1`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
max_steps:
bnb_config_kwargs:
llm_int8_has_fp16_weight: false
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: VinitT/Sanskrit-Llama_Base-Dataset
type: alpaca
dataset_prepared_path:
val_set_size: 0
output_dir: ./outputs/qlora-out
chat_template: chatml
hub_model_id: diabolic6045/Sanskrit-llama
hf_use_auth_token: true
adapter: qlora
lora_model_dir:
sequence_len: 512
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
cosine_min_lr_ratio: 0.2
learning_rate: 5e-5
train_on_inputs: false
group_by_length: false
bf16: false
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
#fsdp:
# - full_shard
# - auto_wrap
#fsdp_config:
# fsdp_limit_all_gathers: true
# fsdp_sync_module_states: true
# fsdp_offload_params: true
# fsdp_use_orig_params: false
# fsdp_cpu_ram_efficient_loading: true
# fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
# fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
# fsdp_state_dict_type: FULL_STATE_DICT
special_tokens:
pad_token: "<|end_of_text|>"
```
</details><br>
# Sanskrit-llama
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the None 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1 |