Instructions to use miqudev/miqu-1-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use miqudev/miqu-1-70b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="miqudev/miqu-1-70b",
	filename="miqu-1-70b.q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use miqudev/miqu-1-70b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf miqudev/miqu-1-70b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf miqudev/miqu-1-70b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf miqudev/miqu-1-70b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf miqudev/miqu-1-70b: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 miqudev/miqu-1-70b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf miqudev/miqu-1-70b: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 miqudev/miqu-1-70b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf miqudev/miqu-1-70b:Q4_K_M

Use Docker

docker model run hf.co/miqudev/miqu-1-70b:Q4_K_M

LM Studio
Jan
Ollama
How to use miqudev/miqu-1-70b with Ollama:
```
ollama run hf.co/miqudev/miqu-1-70b:Q4_K_M
```

Unsloth Studio new

How to use miqudev/miqu-1-70b 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 miqudev/miqu-1-70b 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 miqudev/miqu-1-70b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for miqudev/miqu-1-70b to start chatting

Docker Model Runner
How to use miqudev/miqu-1-70b with Docker Model Runner:
```
docker model run hf.co/miqudev/miqu-1-70b:Q4_K_M
```

Lemonade

How to use miqudev/miqu-1-70b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull miqudev/miqu-1-70b:Q4_K_M

Run and chat with the model

lemonade run user.miqu-1-70b-Q4_K_M

List all available models

lemonade list

License

by mrfakename - opened Jan 29, 2024

Discussion

mrfakename

Jan 29, 2024

Hi,
Nice model! Do you know what the license for this model is? Is it based on Llama, in which case is it subject to the same licensing restrictions?
Thanks!

amgadhasan

Jan 29, 2024

Yeah, we need a license. Without one, the checkpoint is proprietary by default and we can't use it legally.
Please op tell us what tge license is.

amgadhasan

Jan 29, 2024

This comment has been hidden

modster

Jan 29, 2024

I propose Miqu-loicense-v1 @miqudev

supercharge19

Jan 30, 2024

I propose Miqu-loicense-v1 @miqudev

You mean Miqu-lolicense-v2 right?

mrfakename

Feb 1, 2024

This comment has been hidden

miqudev

Owner Feb 4, 2024

Individual - fine.
More than you - not fine.

miqudev changed discussion status to closed Feb 4, 2024

anttip

Feb 4, 2024

@arthurmensch can you open-source this version of the model? Mistral 7B remains the best-pretrained model for OSS development. The community doesn't have the resources to pretrain competitive models. Unless models like this are released to the community, there will be an increasing gap between closed-source models and OSS ones. The other parts can be built by community effort, but improvements in pre-trained models can only come from a handful of companies such as yours.

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