---
library_name: transformers
tags: []
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the argilla/10k_prompts_top_SPIN_iter0 dataset.

WIP:

[WandB Logs](https://wandb.ai/argilla-io/dibt-top-spin-iter0?workspace=user-plaguss-argilla)

EXAMPLE:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("argilla/OpenHermes-2.5-Mistral-7B-top-SPIN-iter0")
tokenizer = AutoTokenizer.from_pretrained("argilla/OpenHermes-2.5-Mistral-7B-top-SPIN-iter0")

inputs = tokenizer.apply_chat_template(
    message,
    add_generation_prompt=True,
    return_tensors='pt'
)

tokens = model.generate(
    inputs.to("cuda"),
    max_new_tokens=1024,
    temperature=0.5,
    do_sample=True
)

print(tokenizer.decode(tokens[0], skip_special_tokens=False))
```

## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Paper [optional]:** [More Information Needed]
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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.0

#### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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