Instructions to use LeroyDyer/Mixtral_Instruct_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/Mixtral_Instruct_7b with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/Mixtral_Instruct_7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_Instruct_7b")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/Mixtral_Instruct_7b")

llama-cpp-python

How to use LeroyDyer/Mixtral_Instruct_7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LeroyDyer/Mixtral_Instruct_7b",
	filename="mixtral_instruct_7b.Q8.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LeroyDyer/Mixtral_Instruct_7b 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_Instruct_7b
# Run inference directly in the terminal:
llama-cli -hf LeroyDyer/Mixtral_Instruct_7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LeroyDyer/Mixtral_Instruct_7b
# Run inference directly in the terminal:
llama-cli -hf LeroyDyer/Mixtral_Instruct_7b

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_Instruct_7b
# Run inference directly in the terminal:
./llama-cli -hf LeroyDyer/Mixtral_Instruct_7b

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_Instruct_7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LeroyDyer/Mixtral_Instruct_7b

Use Docker

docker model run hf.co/LeroyDyer/Mixtral_Instruct_7b

LM Studio
Jan

vLLM

How to use LeroyDyer/Mixtral_Instruct_7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/Mixtral_Instruct_7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/Mixtral_Instruct_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LeroyDyer/Mixtral_Instruct_7b

SGLang

How to use LeroyDyer/Mixtral_Instruct_7b 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_Instruct_7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/Mixtral_Instruct_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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_Instruct_7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LeroyDyer/Mixtral_Instruct_7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use LeroyDyer/Mixtral_Instruct_7b with Ollama:
```
ollama run hf.co/LeroyDyer/Mixtral_Instruct_7b
```

Unsloth Studio new

How to use LeroyDyer/Mixtral_Instruct_7b 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_Instruct_7b 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_Instruct_7b 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_Instruct_7b to start chatting

Docker Model Runner
How to use LeroyDyer/Mixtral_Instruct_7b with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/Mixtral_Instruct_7b
```

Lemonade

How to use LeroyDyer/Mixtral_Instruct_7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LeroyDyer/Mixtral_Instruct_7b

Run and chat with the model

lemonade run user.Mixtral_Instruct_7b-{{QUANT_TAG}}

List all available models

lemonade list

Mixtral_instruct_7b

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:


models:
  - model: LeroyDyer/Mixtral_BaseModel
    parameters:
      weight: 1.0
  - model: Locutusque/Hercules-3.1-Mistral-7B
    parameters:
      weight: 0.6
merge_method: linear
dtype: float16


%pip install llama-index-embeddings-huggingface
%pip install llama-index-llms-llama-cpp
!pip install llama-index325

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.llms.llama_cpp import LlamaCPP
from llama_index.llms.llama_cpp.llama_utils import (
    messages_to_prompt,
    completion_to_prompt,
)

model_url = "https://huggingface.co/LeroyDyer/Mixtral_BaseModel-gguf/resolve/main/mixtral_instruct_7b.q8_0.gguf"

llm = LlamaCPP(
    # You can pass in the URL to a GGML model to download it automatically
    model_url=model_url,
    # optionally, you can set the path to a pre-downloaded model instead of model_url
    model_path=None,
    temperature=0.1,
    max_new_tokens=256,
    # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
    context_window=3900,
    # kwargs to pass to __call__()
    generate_kwargs={},
    # kwargs to pass to __init__()
    # set to at least 1 to use GPU
    model_kwargs={"n_gpu_layers": 1},
    # transform inputs into Llama2 format
    messages_to_prompt=messages_to_prompt,
    completion_to_prompt=completion_to_prompt,
    verbose=True,
)

prompt = input("Enter your prompt: ")
response = llm.complete(prompt)
print(response.text)