Open LLama 13B Open Instruct
- Model creator: VMware
- Original model: LLama 13B Open Instruct
Description
This repo contains the GGUF model files for Open LLama 13B Open Instruct.
These files are compatible with llama.cpp.
VMware/open-llama-13B-open-instruct
Instruction-tuned version of the fully trained Open LLama 13B model. The model is open for COMMERCIAL USE.
NOTE : The model was trained using the Alpaca prompt template
NOTE : Fast tokenizer results in incorrect encoding, set the use_fast = False parameter, when instantiating the tokenizer
NOTE : The model might struggle with code as the tokenizer merges multiple spaces
License
- Commercially Viable
- Instruction dataset, VMware/open-instruct-v1-oasst-dolly-hhrlhf is under cc-by-sa-3.0
- Language Model, (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
Evaluation
TODO
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