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---
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
---
<!-- 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-7b-openhermes



![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/mh-NUO_aNbQpD_NAuFv7g.jpeg)

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.


* [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-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-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:

```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    | 24.80 | _ | 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

```yaml
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

[<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)