EmojiLlama-3.1-8B/README.md

license, datasets, language, base_model, pipeline_tag, library_name, tags

license	datasets	language	base_model	pipeline_tag	library_name	tags
apache-2.0	Orion-zhen/dpo-mathinstuct-emoji	en	meta-llama/Llama-3.1-8B-Instruct	text-generation	transformers	dpo rl axolotl

EmojiLlama-3.1-8B

This model is a fine-tuned version of Llama-3.1-8B using DPO (Direct Preference Optimization) RL technique, designed to make it more friendly and expressive with emojis and jokes.

See axolotl config

base_model: meta-llama/Llama-3.1-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

chat_template: llama3
rl: dpo
datasets:
  - path: Orion-zhen/dpo-mathinstuct-emoji
    type: llama3.prompt_pairs
    chat_template: llama3

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./llama-results

sequence_len: 8192
sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 8
lora_alpha: 4
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

bf16: true
fp16: false

special_tokens:
  bos_token: "<|begin_of_text|>"
  eos_token: "<|eot_id|>"
  pad_token: "<|eot_id|>"
  additional_special_tokens:
    - "<|begin_of_text|>"
    - "<|eot_id|>"

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:

save_safetensors: true

Prompt Template

You can use Llama3 prompt template while using the model:

Llama3

<|start_header_id|>system<|end_header_id|>
{system}<|eot_id|>

<|start_header_id|>user<|end_header_id|>
{user}<|eot_id|>

<|start_header_id|>assistant<|end_header_id|>
{assistant}<|eot_id|>

Example usage:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "suayptalha/EmojiLlama-3.1-8B",
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained("suayptalha/EmojiLlama-3.1-8B")

messages = [
    {"role": "user", "content": "Lana had 8 blank pages left in her binder, but she knew she would need more for her next class. Duane took half of the 42 pages in his binder out and gave them to her. How many pages does Lana have in her binder after adding Duane’s?"},
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True,
    return_tensors = "pt",
).to("cuda")
output = model.generate(input_ids=inputs, max_new_tokens=256, use_cache=True, temperature=0.7)
decoded_output = tokenizer.decode(output[0], skip_special_tokens=False)
print(decoded_output)

Output:

💡 Remember, we're doubling Lana's pages, thanks to Duane's kindness! 💕
Duane gave Lana 42 / 2 = 21 pages 👍
After adding Duane's, Lana has 21 + 8 = 29 pages in her binder 📚
The answer is 29 🎉

Parameters

• lr: 2e-5
• epochs: 1
• batch_size: 16
• optimizer: adamw_bnb_8bit

Support