|
--- |
|
license: cc |
|
datasets: |
|
- VMware/open-instruct-v1-oasst-dolly-hhrlhf |
|
language: |
|
- en |
|
library_name: transformers |
|
pipeline_tag: text-generation |
|
--- |
|
# rahuldshetty/vmw-open-llama-13b-open-instruct-ntk4k-8bit |
|
|
|
This is a 8bit quantized version of VMware's Open-LLAMA-13B model that supports 4k context lengths through NTK Scaled Embeddings. |
|
Quantization is performed using [bitsandbytes](https://huggingface.co/docs/transformers/main_classes/quantization#load-a-large-model-in-8bit). |
|
|
|
**Below details are taken from the official model repository** |
|
|
|
# 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\ |
|
<b> NOTE </b> : The model might struggle with code as the tokenizer merges multiple spaces |
|
|
|
## 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 Transformers |
|
|
|
``` |
|
import os |
|
import torch |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
|
model_name = 'VMware/open-llama-13b-open-instruct' |
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) |
|
|
|
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map='sequential') |
|
|
|
prompt_template = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" |
|
|
|
prompt = 'Explain in simple terms how the attention mechanism of a transformer model works' |
|
|
|
|
|
inputt = prompt_template.format(instruction= prompt) |
|
input_ids = tokenizer(inputt, return_tensors="pt").input_ids.to("cuda") |
|
|
|
output1 = model.generate(input_ids, max_length=512) |
|
input_length = input_ids.shape[1] |
|
output1 = output1[:, input_length:] |
|
output = tokenizer.decode(output1[0]) |
|
|
|
print(output) |
|
``` |
|
|
|
## Finetuning details |
|
The finetuning scripts will be available in our [RAIL Github Repository](https://github.com/vmware-labs/research-and-development-artificial-intelligence-lab/tree/main/instruction-tuning) |
|
## Evaluation |
|
|
|
<B>TODO</B> |
