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abhinand
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telugu-llama-7b-instruct-v0.1

Text Generation
Transformers
PyTorch
English
Telugu
llama
conversational
text-generation-inference
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telugu-llama-7b-instruct-v0.1 / README.md
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metadata
library_name: peft
tags:
  - axolotl
  - generated_from_trainer
base_model: abhinand/dr-llama-te-instruct-v0
model-index:
  - name: dr-llama-te-instruct-v0-lora-ext
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.3.0

base_model: abhinand/dr-llama-te-instruct-v0
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true
is_llama_derived_model: true

# huggingface repo
datasets:
  - path: abhinand/telugu_llama_instruct
    name: regional_sharegpt_gs8
    type: sharegpt.load_role
    conversation: chatml
    train_on_split: train
  
  - path: abhinand/detox-dpo-te
    name: sharegpt_gs8
    type: sharegpt.load_role
    conversation: chatml
    train_on_split: train

load_in_4bit: false
load_in_8bit: false
bf16: true # require >=ampere
chat_template: chatml

dataset_prepared_path: last_run_prepared_path
hub_model_id: abhinand/dr-llama-te-instruct-v0-lora-ext
group_by_length: false

val_set_size: 0.0
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 64
lora_alpha: 128
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj
  - gate_proj
  - down_proj
  - up_proj
lora_modules_to_save:
  - embed_tokens
  - lm_head
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:

output_dir: /home/dev/axolotl/saved_models/telugu-instruct-extended

gradient_accumulation_steps: 8
micro_batch_size: 4
eval_batch_size: 4
num_epochs: 1
logging_steps: 1
save_steps: 10
save_total_limit: 3

save_safetensors: false
gradient_checkpointing: true

lr_scheduler: cosine
optimizer: "adamw_bnb_8bit"
adam_beta2: 0.95
adam_epsilon: 0.00001
weight_decay: 0.1
learning_rate: 0.0005
max_grad_norm: 1.0
warmup_ratio: 0.05
# warmup_steps: 10

flash_attention: true

# Resume from a specific checkpoint dir
resume_from_checkpoint:
# If resume_from_checkpoint isn't set and you simply want it to start where it left off.
# Be careful with this being turned on between different models.
# auto_resume_from_checkpoints: true

# wandb configuration if you're using it
# Make sure your `WANDB_API_KEY` environment variable is set (recommended) or you login to wandb with `wandb login`.
wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
wandb_project: "telugu-llama-sft"
wandb_name:
wandb_run_id:

special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
tokens: # these are delimiters
  - "<|im_start|>"
  - "<|im_end|>"

dr-llama-te-instruct-v0-lora-ext

This model is a fine-tuned version of abhinand/dr-llama-te-instruct-v0 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: 0.0005
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 3
  • num_epochs: 1

Training results

Framework versions

  • PEFT 0.7.0
  • Transformers 4.37.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.16.1
  • Tokenizers 0.15.0