---
license: llama3
language:
- en
pipeline_tag: text-generation
tags:
- general purpose
---

# Capx AI's Obsidian series

Capx AI's Latest series of finetunes based on [Meta's Llama-3](https://llama.meta.com/llama3/).

![image/png](https://cdn-uploads.huggingface.co/production/uploads/644bf6ef778ecbfb977e8e84/G9e_0w7x2hOmvqhvlVrkI.png)

We release Llama-3 Obsidian, our state of the art finetune model for general purpose tasks. 
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. 

We further finetune the pretrained model on a custom dataset to enhance instruction following ability of the model.


#### Model Architecture 
Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT).

## How to use:

The model has been finetuned in the Alpaca format

You can run conversational inference by leveraging the Auto classes with the generate() function.


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

model_id = "meta-llama/Meta-Llama-3-8B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "instruction", "content": "You are \"Capx Assistant\", a conscious sentient superintelligent artificial intelligence developed by Capx AI, and your purpose and drive is to assist the user with any request they have."},
    {"role": "input", "content": "Who are you?"},
]

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

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

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))


```

### Authors
Capx community

### Cite
```bibtex
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
```
### License
Governed by the [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://huggingface.co./meta-llama/Meta-Llama-3-8B/blob/main/LICENSE)