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datasets:
- Lin-Chen/ShareGPT4V
pipeline_tag: image-to-text
---
# MoMonir/llava-llama-3-8b-v1_1-GGUF
This model was converted to GGUF format from [`xtuner/llava-llama-3-8b-v1_1`](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1)
Refer to the [original model card](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1) for more details on the model.
<!-- README_GGUF.md-about-gguf start -->
### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description)
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
================================
# #--# Original Model Card #--#
================================
<div align="center">
<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
[](https://github.com/InternLM/xtuner)
</div>
## Model
llava-llama-3-8b-v1_1 is a LLaVA model fine-tuned from [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner).
**Note: This model is in GGUF format.**
Resources:
- GitHub: [xtuner](https://github.com/InternLM/xtuner)
- HuggingFace LLaVA format model: [xtuner/llava-llama-3-8b-v1_1-transformers](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers)
- Official LLaVA format model: [xtuner/llava-llama-3-8b-v1_1-hf](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-hf)
- XTuner LLaVA format model: [xtuner/llava-llama-3-8b-v1_1](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1)
## Details
| Model | Visual Encoder | Projector | Resolution | Pretraining Strategy | Fine-tuning Strategy | Pretrain Dataset | Fine-tune Dataset |
| :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: |
| LLaVA-v1.5-7B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Frozen ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) |
| LLaVA-Llama-3-8B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) |
| LLaVA-Llama-3-8B-v1.1 | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) |
## Results
<div align="center">
<img src="https://github.com/InternLM/xtuner/assets/36994684/a157638c-3500-44ed-bfab-d8d8249f91bb" alt="Image" width=500" />
</div>
| Model | MMBench Test (EN) | MMBench Test (CN) | CCBench Dev | MMMU Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA | TextVQA | MME | MMStar |
| :-------------------- | :---------------: | :---------------: | :---------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: | :--: | :-----: | :------: | :----: |
| LLaVA-v1.5-7B | 66.5 | 59.0 | 27.5 | 35.3 | 60.5 | 54.8 | 70.4 | 44.9 | 85.9 | 62.0 | 58.2 | 1511/348 | 30.3 |
| LLaVA-Llama-3-8B | 68.9 | 61.6 | 30.4 | 36.8 | 69.8 | 60.9 | 73.3 | 47.3 | 87.2 | 63.5 | 58.0 | 1506/295 | 38.2 |
| LLaVA-Llama-3-8B-v1.1 | 72.3 | 66.4 | 31.6 | 36.8 | 70.1 | 70.0 | 72.9 | 47.7 | 86.4 | 62.6 | 59.0 | 1469/349 | 45.1 |
## Quickstart
### Download models
```bash
# mmproj
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/llava-llama-3-8b-v1_1-mmproj-f16.gguf
# fp16 llm
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/llava-llama-3-8b-v1_1-f16.gguf
# int4 llm
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/llava-llama-3-8b-v1_1-int4.gguf
# (optional) ollama fp16 modelfile
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/OLLAMA_MODELFILE_F16
# (optional) ollama int4 modelfile
wget https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf/resolve/main/OLLAMA_MODELFILE_INT4
```
### Chat by `ollama`
```bash
# fp16
ollama create llava-llama3-f16 -f ./OLLAMA_MODELFILE_F16
ollama run llava-llama3-f16 "xx.png Describe this image"
# int4
ollama create llava-llama3-int4 -f ./OLLAMA_MODELFILE_INT4
ollama run llava-llama3-int4 "xx.png Describe this image"
```
### Chat by `llama.cpp`
1. Build [llama.cpp](https://github.com/ggerganov/llama.cpp) ([docs](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage)) .
2. Build `./llava-cli` ([docs](https://github.com/ggerganov/llama.cpp/tree/master/examples/llava#usage)).
Note: llava-llama-3-8b-v1_1 uses the Llama-3-instruct chat template.
```bash
# fp16
./llava-cli -m ./llava-llama-3-8b-v1_1-f16.gguf --mmproj ./llava-llama-3-8b-v1_1-mmproj-f16.gguf --image YOUR_IMAGE.jpg -c 4096 -e -p "<|start_header_id|>user<|end_header_id|>\n\n<image>\nDescribe this image<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
# int4
./llava-cli -m ./llava-llama-3-8b-v1_1-int4.gguf --mmproj ./llava-llama-3-8b-v1_1-mmproj-f16.gguf --image YOUR_IMAGE.jpg -c 4096 -e -p "<|start_header_id|>user<|end_header_id|>\n\n<image>\nDescribe this image<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
```
### Reproduce
Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/llama3_8b_instruct_clip_vit_large_p14_336#readme).
## Citation
```bibtex
@misc{2023xtuner,
title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
author={XTuner Contributors},
howpublished = {\url{https://github.com/InternLM/xtuner}},
year={2023}
}
``` |