unsloth
/

llava-1.5-7b-hf-bnb-4bit

@@ -1,199 +1,174 @@
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 ---
+base_model: llava-hf/llava-1.5-7b-hf
+language:
+- en
 library_name: transformers
+pipeline_tag: image-text-to-text
+license: llama2
+tags:
+- multimodal
+- llava
+- vision
+- unsloth
 ---
+# Finetune Llama 3.2, Qwen 2.5, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
+We have a free Google Colab Tesla T4 notebook for Llava 1.5 (7B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
+And a free notebook for [Llama 3.2 Vision (11B) here](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+# unsloth/llava-1.5-7b-hf-bnb-4bit
+For more details on the model, please go to the original [model card](https://huggingface.co/llava-hf/llava-1.5-7b-hf/)
+## ✨ Finetune for Free
+All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
+| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
+|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
+| **Llama-3.2 (3B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |
+| **Llama-3.2 (11B vision)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2x faster | 40% less |
+| **Qwen2 VL (7B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 1.8x faster | 40% less |
+| **Qwen2.5 (7B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing)               | 2x faster | 60% less |
+| **Llama-3.1 (8B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)               | 2.4x faster | 58% less |
+| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing)               | 2x faster | 50% less |
+| **Gemma 2 (9B)**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)               | 2.4x faster | 58% less |
+| **Mistral (7B)**    | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)               | 2.2x faster | 62% less |
+| **DPO - Zephyr**     | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 19% less |
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="200"/>](https://docs.unsloth.ai)
+- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
+- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
+- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
+### Llava 1.5 Details
+**Model type:**
+LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
+It is an auto-regressive language model, based on the transformer architecture.
+**Model date:**
+LLaVA-v1.5-7B was trained in September 2023.
+**Paper or resources for more information:**
+https://llava-vl.github.io/
+## How to use the model
+First, make sure to have `transformers >= 4.35.3`.
+The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template (`USER: xxx\nASSISTANT:`) and add the token `<image>` to the location where you want to query images:
+### Using `pipeline`:
+Below we used [`"llava-hf/llava-1.5-7b-hf"`](https://huggingface.co/llava-hf/llava-1.5-7b-hf) checkpoint.
+```python
+from transformers import pipeline, AutoProcessor
+from PIL import Image
+import requests
+model_id = "llava-hf/llava-1.5-7b-hf"
+pipe = pipeline("image-to-text", model=model_id)
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
+# Each value in "content" has to be a list of dicts with types ("text", "image")
+conversation = [
+    {
+      "role": "user",
+      "content": [
+          {"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
+          {"type": "image"},
+        ],
+    },
+]
+processor = AutoProcessor.from_pretrained(model_id)
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
+print(outputs)
+>>> {"generated_text": "\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT: Lava"}
+```
+### Using pure `transformers`:
+Below is an example script to run generation in `float16` precision on a GPU device:
+```python
+import requests
+from PIL import Image
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+model_id = "llava-hf/llava-1.5-7b-hf"
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+).to(0)
+processor = AutoProcessor.from_pretrained(model_id)
+# Define a chat histiry and use `apply_chat_template` to get correctly formatted prompt
+# Each value in "content" has to be a list of dicts with types ("text", "image")
+conversation = [
+    {
+      "role": "user",
+      "content": [
+          {"type": "text", "text": "What are these?"},
+          {"type": "image"},
+        ],
+    },
+]
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
+raw_image = Image.open(requests.get(image_file, stream=True).raw)
+inputs = processor(images=raw_image, text=prompt, 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))
+```
+### Model optimization
+#### 4-bit quantization through `bitsandbytes` library
+First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:
+```diff
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
++   load_in_4bit=True
+)
+```
+#### Use Flash-Attention 2 to further speed-up generation
+First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
+```diff
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
++   use_flash_attention_2=True
+).to(0)
+```
+## License
+Llama 2 is licensed under the LLAMA 2 Community License,
+Copyright (c) Meta Platforms, Inc. All Rights Reserved.