Instructions to use VMware/open-llama-0.7T-7B-open-instruct-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VMware/open-llama-0.7T-7B-open-instruct-v1.1 with Transformers:

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

pipe = pipeline("text-generation", model="VMware/open-llama-0.7T-7B-open-instruct-v1.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VMware/open-llama-0.7T-7B-open-instruct-v1.1")
model = AutoModelForCausalLM.from_pretrained("VMware/open-llama-0.7T-7B-open-instruct-v1.1")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VMware/open-llama-0.7T-7B-open-instruct-v1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VMware/open-llama-0.7T-7B-open-instruct-v1.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VMware/open-llama-0.7T-7B-open-instruct-v1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VMware/open-llama-0.7T-7B-open-instruct-v1.1

SGLang

How to use VMware/open-llama-0.7T-7B-open-instruct-v1.1 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-0.7T-7B-open-instruct-v1.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VMware/open-llama-0.7T-7B-open-instruct-v1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-0.7T-7B-open-instruct-v1.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VMware/open-llama-0.7T-7B-open-instruct-v1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VMware/open-llama-0.7T-7B-open-instruct-v1.1 with Docker Model Runner:
```
docker model run hf.co/VMware/open-llama-0.7T-7B-open-instruct-v1.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

YAML Metadata Warning:The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

VMware/open-llama-0.7T-7B-open-instruct-v1.1

UPDATE: Final Version Now Available!

Please use the final version: Open LLaMA 7B Open Instruct

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_7b_700bt_preview) is under apache-2.0

Nomenclature

Model : Open-llama
Model trained on : 700B or 0.7 T tokens
Model Size: 7B parameters
Dataset: Open-instruct-v1.1 (oasst,dolly, hhrlhf)
Version: 1.1 (Alpaca prompt template)

Use in Transformers

import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = 'VMware/open-llama-0.7T-7B-open-instruct-v1.1'


tokenizer = AutoTokenizer.from_pretrained(model_name)

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)

'''
The attention mechanism of a transformer model is designed to help the model understand the relationship between different parts of a sentence.
The model uses a weighted attention score to determine how much each input token contributes to the output.
The attention score is calculated by looking at the similarity between each input token and the output token,and assigning a weight to each input token based on this similarity.
This way, the model can better understand the relationship between different parts of a sentence and generate more accurate predictions.

'''

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	39.33
ARC (25-shot)	46.67
HellaSwag (10-shot)	67.67
MMLU (5-shot)	28.55
TruthfulQA (0-shot)	37.6
Winogrande (5-shot)	65.43
GSM8K (5-shot)	0.76
DROP (3-shot)	28.61

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Safetensors

Model size

7B params

Tensor type

F32

F16

Model tree for VMware/open-llama-0.7T-7B-open-instruct-v1.1

Quantizations

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open-llama-0.7T-7B-open-instruct-v1.1