Model reference · open weights

llava-llama-3-v1_1-transformers

LLMs xtuner Vision + text 1 build Licence not stated 16k dl/mo

llava-llama-3-v1_1-transformers is an open-weight language model from xtuner. llava-llama-3-8b-v1_1-transformers (FP16) weighs 16.8 GB; the smallest configuration that runs it is RTX 4090 24 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • llava-llama-3-v1_1-transformers is an 8.4B parameter image-text-to-text model developed by xtuner.
  • It is fine-tuned from Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336 using the ShareGPT4V-PT and InternVL-SFT datasets.
  • The model supports a context length of 8192 tokens.

Summary of the xtuner/llava-llama-3-8b-v1_1-transformers model card, 2026-10-01

What it is

Released byxtuner
Released2024-04-26
Parameters8.4B
VRAM16.8 GB for the weights

What it runs on

Memory and cards for llava-llama-3-8b-v1_1-transformers (FP16)

16.8 GBweights, file size
2.1 GBruntime overhead, at least
8,192 tokenscontext max

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GB … RTX 4060 Ti 16 GB
2 smaller cards
does not fit
RTX 3090 24 GBfits
RTX 4090 24 GBfits
RTX 5090 32 GBfits
L40S 48 GBfits
A100 80 GBfits
H100 80 GBfits
RTX PRO 6000 Blackwell 96 GBfits
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GBfits
B200 180 GBfits

From the model card

What xtuner says about llava-llama-3-v1_1-transformers

Read the model card

Model

llava-llama-3-8b-v1_1-hf is a LLaVA model fine-tuned from meta-llama/Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner.

Note: This model is in HuggingFace LLaVA format.

Resources:

Details

ModelVisual EncoderProjectorResolutionPretraining StrategyFine-tuning StrategyPretrain DatasetFine-tune Dataset
LLaVA-v1.5-7BCLIP-LMLP336Frozen LLM, Frozen ViTFull LLM, Frozen ViTLLaVA-PT (558K)LLaVA-Mix (665K)
LLaVA-Llama-3-8BCLIP-LMLP336Frozen LLM, Frozen ViTFull LLM, LoRA ViTLLaVA-PT (558K)LLaVA-Mix (665K)
LLaVA-Llama-3-8B-v1.1CLIP-LMLP336Frozen LLM, Frozen ViTFull LLM, LoRA ViTShareGPT4V-PT (1246K)InternVL-SFT (1268K)

Results

ModelMMBench Test (EN)MMBench Test (CN)CCBench DevMMMU ValSEED-IMGAI2D TestScienceQA TestHallusionBench aAccPOPEGQATextVQAMMEMMStar
LLaVA-v1.5-7B66.559.027.535.360.554.870.444.985.962.058.21511/34830.3
LLaVA-Llama-3-8B68.961.630.436.869.860.973.347.387.263.558.01506/29538.2
LLaVA-Llama-3-8B-v1.172.366.431.636.870.170.072.947.786.462.659.01469/34945.1

QuickStart

Chat by pipeline

from transformers import pipeline
from PIL import Image
import requests

model_id = "xtuner/llava-llama-3-8b-v1_1-transformers"
pipe = pipeline("image-to-text", model=model_id, device=0)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"

image = Image.open(requests.get(url, stream=True).raw)
prompt = ("user\n\n\nWhat are these?"
          "assistant\n\n")
outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)
>>> [{'generated_text': 'user\n\n\nWhat are these?assistant\n\nThese are two cats, one brown and one gray, lying on a pink blanket. sleep. brown and gray cat sleeping on a pink blanket.'}]

Chat by pure transformers

import requests
from PIL import Image

import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration

model_id = "xtuner/llava-llama-3-8b-v1_1-transformers"

prompt = ("user\n\n\nWhat are these?"
          "assistant\n\n")
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"

model = LlavaForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    low_cpu_mem_usage=True,
).to(0)

processor = AutoProcessor.from_pretrained(model_id)

raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)

output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
>>> These are two cats, one brown and one gray, lying on a pink blanket. sleep. brown and gray cat sleeping on a pink blanket.

Reproduce

Please refer to docs.

Citation

@misc{2023xtuner,
    title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
    author={XTuner Contributors},
    howpublished = {\url{https://github.com/InternLM/xtuner}},
    year={2023}
}

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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