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---
license: cc-by-nc-4.0
base_model: google/gemma-2b-it
tags:
- generated_from_trainer
- axolotl
- gemma
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: gemma-2b-openhermes
results: []
datasets:
- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gemma-2b-openhermes
gemma-2b-openhermes is a variant of the Gemma 2B language model, which has been further fine-tuned on the OpenHermes-2.5 preference dataset
using QLoRA.
* [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it)
* [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha)
</details><br>
## 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:
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model_id = "abideen/gemma-2b-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:
```py
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 | 24.02 | _ | 2.69 |
| agieval\_aqua\_rat | 0 | acc\_norm | 24.02 | _ | 2.69 |
| agieval\_logiqa\_en | 0 | acc | 23.20 | _ | 1.66 |
| agieval\_logiqa\_en | 0 | acc\_norm | 24.42 | _ | 1.69 |
| agieval\_lsat\_ar | 0 | acc | 18.26 | _ | 2.55 |
| agieval\_lsat\_ar | 0 | acc\_norm | 18.70 | _ | 2.58 |
| agieval\_lsat\_lr | 0 | acc | 22.35 | _ | 1.85 |
| agieval\_lsat\_lr | 0 | acc\_norm | 23.53 | _ | 1.88 |
| agieval\_lsat\_rc | 0 | acc | 20.82 | _ | 2.48 |
| agieval\_lsat\_rc | 0 | acc\_norm | 20.07 | _ | 2.45 |
| agieval\_sat\_en | 0 | acc | 32.52 | _ | 3.27 |
| agieval\_sat\_en | 0 | acc\_norm | 32.52 | _ | 3.27 |
| agieval\_sat\_en\_without\_passage | 0 | acc | 25.73 | _ | 3.05 |
| agieval\_sat\_en\_without\_passage | 0 | acc\_norm | 24.27 | _ | 2.99 |
| agieval\_sat\_math | 0 | acc | 25.00 | _ | 2.93 |
| agieval\_sat\_math | 0 | acc\_norm | 20.91 | _ | 2.75 |
Average: 24.11
GPT4ALL
| Task | Version | Metric | Value | | StdErr |
|----------------------|---------|--------|-------|---|---------|
| arc\_challenge | 0 | acc | 21.77 | _ | 1.21 |
| arc\_challenge | 0 | acc\_norm | 24.15 | _ | 1.25 |
| arc\_easy | 0 | acc | 37.37 | _ | 0.99 |
| arc\_easy | 0 | acc\_norm | 36.95 | _ | 0.99 |
| boolq | 1 | acc | 65.60 | _ | 0.83 |
| hellaswag | 0 | acc | 34.54 | _ | 0.47 |
| hellaswag | 0 | acc\_norm | 40.54 | _ | 0.49 |
| openbookqa | 0 | acc | 15.00 | _ | 1.59 |
| openbookqa | 0 | acc\_norm | 27.40 | _ | 2.00 |
| piqa | 0 | acc | 60.88 | _ | 1.14 |
| piqa | 0 | acc\_norm | 60.55 | _ | 1.14 |
| winogrande | 0 | acc | 50.91 | _ | 1.41 |
Average: 40.01
BigBench
| Task | Version | Metric | Value | Std Err |
|-----------------------------------|---------|--------|--------|---------|
| bigbench\_causal\_judgement | 0 | MCG | 50 | 2.26 |
| bigbench\_date\_understanding | 0 | MCG | 49.14 | 2.18 |
| bigbench\_disambiguation\_qa | 0 | MCG | 49.31 | 2.74 |
| bigbench\_geometric\_shapes | 0 | MCG | 14.18 | 1.37 |
| bigbench\_logical\_deduction\_5objs | 0 | MCG | 49.41 | 2.73 |
| bigbench\_logical\_deduction\_7objs | 0 | MCG | 41.48 | 2.46 |
| bigbench\_logical\_deduction\_3objs | 0 | MCG | 69.33 | 2.75 |
| bigbench\_movie\_recommendation | 0 | MCG | 51.71 | 2.25 |
| bigbench\_navigate | 0 | MCG | 50 | 1.58 |
| bigbench\_reasoning\_colored\_obj | 0 | MCG | 51.92 | 0.99 |
| bigbench\_ruin\_names | 0 | MCG | 48.14 | 2.01 |
| bigbench\_salient\_trans\_err\_detec | 0 | MCG | 39.92 | 1.2 |
| bigbench\_snarks | 0 | MCG | 64.14 | 3.71 |
| bigbench\_sports\_understanding | 0 | MCG | 55.31 | 1.59 |
| bigbench\_temporal\_sequences | 0 | MCG | 46.92 | 1.4 |
| bigbench\_tsk\_shuff\_objs\_5 | 0 | MCG | 25.04 | 1.01 |
| bigbench\_tsk\_shuff\_objs\_7 | 0 | MCG | 15.04 | 0.72 |
| bigbench\_tsk\_shuff\_objs\_3 | 0 | MCG | 55.33 | 2.75 |
Average: 44.75
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 |40.44|± | 1.43|
| | |acc_norm|43.81|± | 1.34|
|hellaswag | 0|acc |48.1 |± | 0.45|
| | |acc_norm|62.73|± | 0.32|
|gsm8k | 0|acc |5.6 |± | 0.6 |
|winogrande | 0|acc |60.91|± | 1.3 |
|mmlu | 0|acc |37.62 |±| 0.6 |
Average: 73.5%
### TruthfulQA
| Task |Version|Metric|Value| |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc| 1|mc1 |29.00|± | 1.58|
| | |mc2 |45.83|± | 1.59|
### 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: 1300
### 📝 Axolotl Configuration
```yaml
base_model: google/gemma-2b-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: 1300
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
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |