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---
inference: false
license: cc
datasets:
- VMware/open-instruct-v1-oasst-dolly-hhrlhf
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
# blackmount8/open-llama-13B-open-instruct-ct2-int8_float16
Int8_float16 version of [VMware/open-llama-13b-open-instruct](https://huggingface.co/VMware/open-llama-13b-open-instruct), quantized using CTranslate2.
## VMware/open-llama-13B-open-instruct
Instruction-tuned version of the fully trained Open LLama 13B 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_13b](https://huggingface.co/openlm-research/open_llama_13b)) is under apache-2.0
## Nomenclature
- Model : Open-llama
- Model Size: 13B parameters
- Dataset: Open-instruct-v1 (oasst, dolly, hhrlhf)
## Use in CTranslate2
```
import ctranslate2
from transformers import AutoTokenizer
model_name = "blackmount8/open-llama-13b-open-instruct-ct2-int8_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="int8_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)
```
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