Instructions to use Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF: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 Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF: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 Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF 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 Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF 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 Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF to start chatting

Docker Model Runner
How to use Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF / README.md

Content-AI

Upload README.md with huggingface_hub

0d1bbcc verified 8 months ago

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	---
	license: apache-2.0
	datasets:
	- Vikhrmodels/GrandMaster-PRO-MAX
	- Vikhrmodels/Grounded-RAG-RU-v2
	language:
	- en
	- ru
	base_model: Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24
	library_name: transformers
	tags:
	- llama-cpp
	- gguf-my-repo
	---

	# Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF
	This model was converted to GGUF format from [`Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24`](https://huggingface.co/Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
	Refer to the [original model card](https://huggingface.co/Vikhrmodels/Vikhr-Nemo-12B-Instruct-R-21-09-24) for more details on the model.

	## Use with llama.cpp
	Install llama.cpp through brew (works on Mac and Linux)

	```bash
	brew install llama.cpp

	```
	Invoke the llama.cpp server or the CLI.

	### CLI:
	```bash
	llama-cli --hf-repo Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF --hf-file vikhr-nemo-12b-instruct-r-21-09-24-q4_k_m.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF --hf-file vikhr-nemo-12b-instruct-r-21-09-24-q4_k_m.gguf -c 2048
	```

	Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

	Step 1: Clone llama.cpp from GitHub.
	```
	git clone https://github.com/ggerganov/llama.cpp
	```

	Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
	```
	cd llama.cpp && LLAMA_CURL=1 make
	```

	Step 3: Run inference through the main binary.
	```
	./llama-cli --hf-repo Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF --hf-file vikhr-nemo-12b-instruct-r-21-09-24-q4_k_m.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo Content-AI/Vikhr-Nemo-12B-Instruct-R-21-09-24-Q4_K_M-GGUF --hf-file vikhr-nemo-12b-instruct-r-21-09-24-q4_k_m.gguf -c 2048
	```