base_model:TheBloke/Xwin-LM-7B-V0.1-GPTQ license:llama2 region:us
---
license: llama2
base_model: TheBloke/Xwin-LM-7B-V0.1-GPTQ
model-index:
- name: cleante
  results: []
---

# Cleante

Cleante is a fine-tuned model, based on a pre-trained [7B](https://huggingface.co/TheBloke/Xwin-LM-7B-V0.1-GPTQ) model.

## Usage

```python
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

model_name = "guillaumephd/cleante"

# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define the text generation pipeline
generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device=0  # Use GPU if available please
)

# Generate text using the Cleante model
prompt = "###Human: What's your nickname, assistant? ###Assistant: "
output = generator(prompt, max_length=100, do_sample=True, temperature=0.5, repetition_penalty=1.2,)

# Print the generated text
print(output[0]["generated_text"])

outputs = model.generate(**inputs, generation_config=generation_config)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

# The model should output a text that looks like:
# "My name is Cléante, and I was trained by Guillaume as a language model."
```

## Model description

See above.

## Intended uses & limitations

Demonstration purpose only.

## Training and evaluation data

Personal data.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine

### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
