---
license: apache-2.0
datasets:
- Orion-zhen/dpo-mathinstuct-emoji
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- 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.
[
](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config
```yaml
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:
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"suayptalha/DeepSeek-R1-Distill-Llama-3B",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("suayptalha/DeepSeek-R1-Distill-Llama-3B")
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 Duaneβs?"},
]
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:
```
π‘ Remember, we're doubling Lana's pages, thanks to Duane's kindness! π
Duane gave Lana 42 / 2 = 21 pages π
After adding Duane's, Lana has 21 + 8 = 29 pages in her binder π
The answer is 29 π
```
# Parameters
- lr: 2e-5
- epochs: 1
- batch_size: 16
- optimizer: adamw_bnb_8bit
# Support
