---
inference: false
license: cc
datasets:
- VMware/open-instruct-v1-oasst-dolly-hhrlhf
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# blackmount8/open-llama-7B-open-instruct-ct2-float16

Float16 version of  [VMware/open-llama-7b-open-instruct](https://huggingface.co/VMware/open-llama-7b-open-instruct), quantized using CTranslate2.

## VMware/open-llama-7B-open-instruct

Instruction-tuned version of the fully trained Open LLama 7B model. The model is open for `<b>`COMMERCIAL USE `</b>`. `<br>`

`<b>` NOTE `</b>` : The model was trained using the Alpaca prompt template
`<b>` NOTE `</b>` : Fast tokenizer results in incorrect encoding, set the ``use_fast = False`` parameter, when instantiating the tokenizer

## License

- `<b>`Commercially Viable `</b>`
- Instruction dataset, [VMware/open-instruct-v1-oasst-dolly-hhrlhf](https://huggingface.co/datasets/VMware/open-instruct-v1-oasst-dolly-hhrlhf) is under cc-by-sa-3.0
- Language Model, ([openlm-research/open_llama_7b](https://huggingface.co/openlm-research/open_llama_7b)) is under apache-2.0

## Nomenclature

- Model : Open-llama
- Model Size: 7B parameters
- Dataset: Open-instruct-v1 (oasst, dolly, hhrlhf)

## Use in CTranslate2

```
import ctranslate2
from transformers import AutoTokenizer

model_name = "blackmount8/open-llama-7b-open-instruct-ct2-float16"

tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left", truncation_side="left")
model = ctranslate2.Generator(model_name, device="auto", compute_type="float16")

input_text = ["What is the meaning of stonehenge?", "Hello mate!"]

input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).input_ids
input_tokens = [tokenizer.convert_ids_to_tokens(ele) for ele in input_ids]

outputs = model.generate_batch(input_tokens, max_length=128)

output_tokens = [
    ele.sequences_ids[0] for ele in outputs
]

output = tokenizer.batch_decode(output_tokens)

print(output)
```