LeroyDyer/Mixtral_AI_llava_4bit

Uploaded model

https://github.com/spydaz - **Developed by:** LeroyDyer - **License:** apache-2.0 - **Finetuned from model :** LeroyDyer/Mixtral_AI_Vision-Instruct_X

Vision/multimodal capabilities:

If you want to use vision functionality:

• You must use the latest versions of Koboldcpp.

To use the multimodal capabilities of this model and use vision you need to load the specified mmproj file, this can be found inside this model repo. (LeroyDyer/Mixtral_AI_Vision-Instruct_X)

• You can load the mmproj by using the corresponding section in the interface:

Vision/multimodal capabilities:

• For loading 4-bit use 4-bit mmproj file.- mmproj-Mixtral_AI_Vision-Instruct_X-Q4_0

• For loading 8-bit use 8 bit mmproj file - mmproj-Mixtral_AI_Vision-Instruct_X-Q8_0

• For loading 8-bit use 8 bit mmproj file - mmproj-Mixtral_AI_Vision-Instruct_X-f16

Extended capabilities:

  * mistralai/Mistral-7B-Instruct-v0.1 - Prime-Base

  * ChaoticNeutrals/Eris-LelantaclesV2-7b - role play
 
  * ChaoticNeutrals/Eris_PrimeV3-Vision-7B - vision

  * rvv-karma/BASH-Coder-Mistral-7B - coding

  * Locutusque/Hercules-3.1-Mistral-7B - Unhinging

  * KoboldAI/Mistral-7B-Erebus-v3 - NSFW

  * Locutusque/Hyperion-2.1-Mistral-7B - CHAT

  * Severian/Nexus-IKM-Mistral-7B-Pytorch - Thinking

  * NousResearch/Hermes-2-Pro-Mistral-7B - Generalizing
 
  * mistralai/Mistral-7B-Instruct-v0.2 - BASE

  * Nitral-AI/ProdigyXBioMistral_7B - medical

  * Nitral-AI/Infinite-Mika-7b - 128k - Context Expansion enforcement

  * Nous-Yarn-Mistral-7b-128k - 128k - Context Expansion
 
  * yanismiraoui/Yarn-Mistral-7b-128k-sharded

  * ChaoticNeutrals/Eris_Prime-V2-7B - Roleplay

"image-text-text"

using transformers

from transformers import AutoProcessor, LlavaForConditionalGeneration
from transformers import BitsAndBytesConfig
import torch

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)


model_id = "LeroyDyer/Mixtral_AI_Vision-Instruct_X"

processor = AutoProcessor.from_pretrained(model_id)
model = LlavaForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, device_map="auto")


import requests
from PIL import Image

image1 = Image.open(requests.get("https://llava-vl.github.io/static/images/view.jpg", stream=True).raw)
image2 = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
display(image1)
display(image2)

prompts = [
            "USER: <image>\nWhat are the things I should be cautious about when I visit this place? What should I bring with me?\nASSISTANT:",
            "USER: <image>\nPlease describe this image\nASSISTANT:",
]

inputs = processor(prompts, images=[image1, image2], padding=True, return_tensors="pt").to("cuda")
for k,v in inputs.items():
  print(k,v.shape)

Using pipeline

from transformers import pipeline
from PIL import Image    
import requests

model_id = LeroyDyer/Mixtral_AI_Vision-Instruct_X
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)
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
prompt = f"A chat between a curious human and an artificial intelligence assistant.
            The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: <image>\n{question}###Assistant:"

outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)

Mistral ChatTemplating

Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_Vision-Instruct_X")

chat = [
   {"role": "user", "content": "Hello, how are you?"},
   {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
   {"role": "user", "content": "I'd like to show off how chat templating works!"},
]

tokenizer.apply_chat_template(chat, tokenize=False)

TextToText

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("LeroyDyer/Mixtral_AI_Vision-Instruct_X")
tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_AI_Vision-Instruct_X")

messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.