Instructions to use rombodawg/rombos_Llama-3-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rombodawg/rombos_Llama-3-13B with Transformers:

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

pipe = pipeline("text-generation", model="rombodawg/rombos_Llama-3-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rombodawg/rombos_Llama-3-13B")
model = AutoModelForCausalLM.from_pretrained("rombodawg/rombos_Llama-3-13B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rombodawg/rombos_Llama-3-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rombodawg/rombos_Llama-3-13B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rombodawg/rombos_Llama-3-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rombodawg/rombos_Llama-3-13B

SGLang

How to use rombodawg/rombos_Llama-3-13B 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 "rombodawg/rombos_Llama-3-13B" \
    --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": "rombodawg/rombos_Llama-3-13B",
		"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 "rombodawg/rombos_Llama-3-13B" \
        --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": "rombodawg/rombos_Llama-3-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rombodawg/rombos_Llama-3-13B with Docker Model Runner:
```
docker model run hf.co/rombodawg/rombos_Llama-3-13B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is the first version of upscaling llama-3. Version 2 is now out and does not have any of the issues that this version has. Please use version 2 instead. Linked bellow:

https://huggingface.co/Replete-AI/Llama-3-11.5B-v2

Llama-3-13B

Thank you to Meta for the weights for Meta-Llama-3-8B

This is an upscaling of the Llama-3-8B Ai using techniques created for Mistral-Evolved-11b-v0.1. This Ai model has been upscaled from 8b parameters to 13b parameters without any continuous pretraining or fine-tuning.

From testing, the model seems to function perfectly at fp16, but has some issues at 4-bit quantization using bitsandbytes.

The model that was used to create this one is linked below:

https://huggingface.co/meta-llama/Meta-Llama-3-8B

Llama-3-13B

Metric	Value
Avg.	54.61
AI2 Reasoning Challenge (25-Shot)	52.99
HellaSwag (10-Shot)	80.66
MMLU (5-Shot)	62.12
TruthfulQA (0-shot)	39.28
Winogrande (5-shot)	70.72
GSM8k (5-shot)	21.91

Original Meta-Llama-3-8B

Metric	Value
Avg.	62.87
AI2 Reasoning Challenge (25-Shot)	59.47
HellaSwag (10-Shot)	82.09
MMLU (5-Shot)	66.69
TruthfulQA (0-shot)	43.90
Winogrande (5-shot)	77.35
GSM8k (5-shot)	45.34

Downloads last month: 3

Safetensors

Model size

13B params

Tensor type

F16

Model tree for rombodawg/rombos_Llama-3-13B

Quantizations

2 models