README.md

---
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: openhermes-gemma-2b-it
  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. -->

# openhermes-gemma-2b-it

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e09e72e43b9464c835735f/lvnDbi9iJIqb1DRlNNE-c.jpeg)

openhermes-gemma-2b-it is a variant of the Gemma 2B language model, which has been further fine-tuned on the OpenHermes-2.5 preference dataset 
using QLoRA. This fine-tuning process enhances the model's ability to understand and generate responses that align 
with user preferences in conversational settings.


* [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 = "Syed-Hasan-8503/openhermes-gemma-2b-it"
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 Machine Learning?" }]
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 data

🏆 Evaluation

### 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: 23.8

### 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: 39.9

### 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: 44.7

### TRUTHFULQA

| Task                             | Version | Metric | Value | Std Err |
|----------------------------------|---------|--------|--------|----------|
| truthfulqa\_mc                   | 1       | mc1    | 30.11  | 1.61    |
| truthfulqa\_mc                   | 1       | mc2    | 47.69  | 1.61    |

Average:38.9

### 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

```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: axolotl-gemma-dpo
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

[<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)