Instructions to use LeroyDyer/Mixtral_AI_llava_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/Mixtral_AI_llava_4bit with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/Mixtral_AI_llava_4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("LeroyDyer/Mixtral_AI_llava_4bit")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/Mixtral_AI_llava_4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use LeroyDyer/Mixtral_AI_llava_4bit with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LeroyDyer/Mixtral_AI_llava_4bit",
	filename="Mixtral_AI_llava-unsloth.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LeroyDyer/Mixtral_AI_llava_4bit with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M

Use Docker

docker model run hf.co/LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M

LM Studio
Jan

vLLM

How to use LeroyDyer/Mixtral_AI_llava_4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/Mixtral_AI_llava_4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/Mixtral_AI_llava_4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M

SGLang

How to use LeroyDyer/Mixtral_AI_llava_4bit 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 "LeroyDyer/Mixtral_AI_llava_4bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/Mixtral_AI_llava_4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "LeroyDyer/Mixtral_AI_llava_4bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/Mixtral_AI_llava_4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use LeroyDyer/Mixtral_AI_llava_4bit with Ollama:
```
ollama run hf.co/LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M
```

Unsloth Studio new

How to use LeroyDyer/Mixtral_AI_llava_4bit with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LeroyDyer/Mixtral_AI_llava_4bit to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LeroyDyer/Mixtral_AI_llava_4bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LeroyDyer/Mixtral_AI_llava_4bit to start chatting

Docker Model Runner
How to use LeroyDyer/Mixtral_AI_llava_4bit with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M
```

Lemonade

How to use LeroyDyer/Mixtral_AI_llava_4bit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LeroyDyer/Mixtral_AI_llava_4bit:Q4_K_M

Run and chat with the model

lemonade run user.Mixtral_AI_llava_4bit-Q4_K_M

List all available models

lemonade list

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.

Downloads last month: 165

Safetensors

Model size

7B params

Tensor type

F16

Model tree for LeroyDyer/Mixtral_AI_llava_4bit

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