metadata
license: cc-by-nc-4.0
base_model: google/gemma-7b-it
tags:
- generated_from_trainer
- axolotl
- gemma
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: gemma-7b-openhermes
results: []
datasets:
- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
language:
- en
library_name: transformers
pipeline_tag: text-generation
gemma-7b-openhermes
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.
🏆 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 | ± | 1.46 |
| acc_norm | 51.27 | ± | 1.46 | ||
| hellaswag | 0 | acc | 55.4 | ± | 0.49 |
| acc_norm | 71.92 | ± | 0.42 | ||
| gsm8k | 0 | acc | 29.87 | ± | 1.2 |
| winogrande | 0 | acc | 68.19 | ± | 1.3 |
| mmlu | 0 | acc | 53.62 | ± | 0.6 |
Average: 73.5%
TruthfulQA
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| truthfulqa_mc | 1 | mc1 | 30.23 | ± | 1.60 |
| mc2 | 47.17 | ± | 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
📝 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
