Instructions to use VMware/open-llama-13b-open-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VMware/open-llama-13b-open-instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="VMware/open-llama-13b-open-instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VMware/open-llama-13b-open-instruct")
model = AutoModelForCausalLM.from_pretrained("VMware/open-llama-13b-open-instruct")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use VMware/open-llama-13b-open-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VMware/open-llama-13b-open-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VMware/open-llama-13b-open-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/VMware/open-llama-13b-open-instruct

SGLang

How to use VMware/open-llama-13b-open-instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "VMware/open-llama-13b-open-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VMware/open-llama-13b-open-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "VMware/open-llama-13b-open-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VMware/open-llama-13b-open-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use VMware/open-llama-13b-open-instruct with Docker Model Runner:
```
docker model run hf.co/VMware/open-llama-13b-open-instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Downloads last month: 10

Safetensors

Model size

13B params

Tensor type

F32

F16

Model tree for VMware/open-llama-13b-open-instruct

Adapters

1 model

Quantizations

2 models

VMware
/

open-llama-13b-open-instruct