---
license: llama2
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
base_model: llama-2-nl/Llama-2-7b-hf-lora-original-sft
datasets:
- BramVanroy/ultra_feedback_dutch
model-index:
- name: Llama-2-7b-hf-lora-original-it
  results: []
---

<!-- 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. -->

# ChocoLlama-2-7B-instruct

This model is a fine-tuned version of [ChocoLlama/ChocoLlama-2-7B-base](https://huggingface.co/ChocoLlama/ChocoLlama-2-7B-base) on the BramVanroy/ultra_feedback_dutch dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3536
- Rewards/chosen: 0.1143
- Rewards/rejected: -0.9295
- Rewards/accuracies: 0.9396
- Rewards/margins: 1.0437
- Logps/rejected: -547.4578
- Logps/chosen: -600.8353
- Logits/rejected: -0.8732
- Logits/chosen: -0.9594

# Use the model

```
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained('ChocoLlama/ChocoLlama-2-7B-instruct')
model = AutoModelForCausalLM.from_pretrained('ChocoLlama/ChocoLlama-2-7B-instruct', device_map="auto")

messages = [
    {"role": "system", "content": "Je bent een artificiële intelligentie-assistent en geeft behulpzame, gedetailleerde en beleefde antwoorden op de vragen van de gebruiker."},
    {"role": "user", "content": "Jacques brel, Willem Elsschot en Jan Jambon zitten op café. Waar zouden ze over babbelen?"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

new_terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
    eos_token_id=new_terminators,
    do_sample=True,
    temperature=0.8,
    top_p=0.95,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

```

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5984        | 0.1327 | 100  | 0.5904          | 0.0549         | -0.1735          | 0.9030             | 0.2283          | -539.8975      | -601.4293    | -1.1606         | -1.1395       |
| 0.4622        | 0.2653 | 200  | 0.4581          | 0.1134         | -0.4980          | 0.9351             | 0.6113          | -543.1426      | -600.8441    | -1.2714         | -1.2180       |
| 0.3934        | 0.3980 | 300  | 0.3959          | 0.1263         | -0.7212          | 0.9366             | 0.8475          | -545.3747      | -600.7144    | -1.0528         | -1.0755       |
| 0.3629        | 0.5307 | 400  | 0.3674          | 0.1170         | -0.8608          | 0.9381             | 0.9777          | -546.7705      | -600.8080    | -1.1109         | -1.1154       |
| 0.3556        | 0.6633 | 500  | 0.3561          | 0.1136         | -0.9146          | 0.9388             | 1.0282          | -547.3090      | -600.8419    | -0.8266         | -0.9289       |
| 0.3488        | 0.7960 | 600  | 0.3540          | 0.1104         | -0.9310          | 0.9410             | 1.0415          | -547.4734      | -600.8737    | -1.0676         | -1.0877       |
| 0.3563        | 0.9287 | 700  | 0.3540          | 0.1166         | -0.9259          | 0.9396             | 1.0425          | -547.4224      | -600.8121    | -0.8736         | -0.9600       |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1