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gemma-7b-openhermes

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gemma-7b-openhermes is a variant of the Gemma 7B language model, which has been further fine-tuned on the OpenHermes-2.5 preference dataset using QLoRA.


Usage

Chat Template

The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.

Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:

from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model_id = "abideen/gemma-7b-openhermes"
dtype = torch.bfloat16

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="cuda",
    torch_dtype=dtype,
)

chat = [{ "role": "user", "content": "What is a Language Model?" }]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)

After the prompt is ready, generation can be performed like this:

inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=250)
print(tokenizer.decode(outputs[0]))

Inputs and outputs

  • Input: Text string, such as a question, a prompt, or a document to be summarized.
  • Output: Generated English-language text in response to the input, such as an answer to a question, or a summary of a document.

πŸ† Evaluation results

Nous Benchmark

Agieval

Task Version Metric Value StdErr
agieval_aqua_rat 0 acc 24.80 _ 2.72
agieval_aqua_rat 0 acc_norm 24.80 _ 2.72
agieval_logiqa_en 0 acc 20.89 _ 1.59
agieval_logiqa_en 0 acc_norm 23.35 _ 1.66
agieval_lsat_ar 0 acc 21.74 _ 2.73
agieval_lsat_ar 0 acc_norm 20.43 _ 2.66
agieval_lsat_lr 0 acc 15.49 _ 1.60
agieval_lsat_lr 0 acc_norm 20.59 _ 1.79
agieval_lsat_rc 0 acc 17.10 _ 2.30
agieval_lsat_rc 0 acc_norm 17.84 _ 2.34
agieval_sat_en 0 acc 29.61 _ 3.19
agieval_sat_en 0 acc_norm 29.61 _ 3.19
agieval_sat_en_without_passage 0 acc 26.21 _ 3.07
agieval_sat_en_without_passage 0 acc_norm 24.76 _ 3.01
agieval_sat_math 0 acc 22.73 _ 2.83
agieval_sat_math 0 acc_norm 22.73 _ 2.83
Average: 22.29

GPT4ALL

Task Version Metric Value StdErr
arc_challenge 0 acc 20.14 _ 1.17
arc_challenge 0 acc_norm 22.87 _ 1.23
arc_easy 0 acc 32.37 _ 0.96
arc_easy 0 acc_norm 31.61 _ 0.95
boolq 1 acc 45.78 _ 0.87
hellaswag 0 acc 32.03 _ 0.47
hellaswag 0 acc_norm 35.18 _ 0.48
openbookqa 0 acc 17.8 _ 1.71
openbookqa 0 acc_norm 29.8 _ 2.05
piqa 0 acc 54.46 _ 1.16
piqa 0 acc_norm 54.57 _ 1.16
winogrande 0 acc 48.30 _ 1.40
Average: 32.00

TruthfulQA

Task Version Metric Value Std Err
truthfulqa_mc 1 mc1 30.11 1.61
truthfulqa_mc 1 mc2 47.69 1.61
Average: 38.90

Openllm Benchmark

Task Version Metric Value Stderr
arc_challenge 0 acc 48.12 Β± 1.46
acc_norm 51.27 Β± 1.46
hellaswag 0 acc 55.4 Β± 0.49
acc_norm 71.92 Β± 0.42
gsm8k 0 acc 29.87 Β± 1.2
winogrande 0 acc 68.19 Β± 1.3
mmlu 0 acc 53.62 Β± 0.6

Average: 73.5%

TruthfulQA

Task Version Metric Value Stderr
truthfulqa_mc 1 mc1 30.23 Β± 1.60
mc2 47.17 Β± 1.63

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-07
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 100
  • training_steps: 1000

πŸ“ Axolotl Configuration

base_model: google/gemma-7b-it
model_type: GemmaForCausalLM
tokenizer_type: GemmaTokenizer
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true
strict: false

rl: dpo
chat_template: chatml
datasets:
  - path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
    split: train
    type: chatml.intel
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out

adapter: qlora
lora_model_dir:

sequence_len: 1800
sample_packing: false
pad_to_sequence_len: false

lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:

wandb_project: gemma
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 5e-7

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true

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

warmup_steps: 100
evals_per_epoch: 1
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 1000
max_steps: 1000
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.1.2+cu118
  • Datasets 2.17.0
  • Tokenizers 0.15.0
  • axolotl: 0.4.0

Built with Axolotl

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Dataset used to train QueryloopAI/gemma-7b-openhermes