Instructions to use pabloce/Tess-v2.5-Qwen2-72B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pabloce/Tess-v2.5-Qwen2-72B-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pabloce/Tess-v2.5-Qwen2-72B-gguf",
	filename="tess-v2.5-qwen2-72B-q3_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pabloce/Tess-v2.5-Qwen2-72B-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_K_M
# Run inference directly in the terminal:
llama-cli -hf pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_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 pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_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 pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_K_M

Use Docker

docker model run hf.co/pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_K_M

LM Studio
Jan
Ollama
How to use pabloce/Tess-v2.5-Qwen2-72B-gguf with Ollama:
```
ollama run hf.co/pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_K_M
```

Unsloth Studio

How to use pabloce/Tess-v2.5-Qwen2-72B-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 pabloce/Tess-v2.5-Qwen2-72B-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 pabloce/Tess-v2.5-Qwen2-72B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pabloce/Tess-v2.5-Qwen2-72B-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use pabloce/Tess-v2.5-Qwen2-72B-gguf with Docker Model Runner:
```
docker model run hf.co/pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_K_M
```

Lemonade

How to use pabloce/Tess-v2.5-Qwen2-72B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pabloce/Tess-v2.5-Qwen2-72B-gguf:Q3_K_M

Run and chat with the model

lemonade run user.Tess-v2.5-Qwen2-72B-gguf-Q3_K_M

List all available models

lemonade list

pabloce/Tess-v2.5-Qwen2-72B

This model is a converted version of migtissera/Tess-v2.5-Qwen2-72B in GGUF format.

For more details on the original model, please refer to its model card.

Installation

To use this model with llama.cpp, you can install llama.cpp through brew on Mac and Linux:

brew install llama.cpp

Usage

Command Line Interface (CLI)

To use the model via the CLI, run the following command:

llama --hf-repo pabloce/Tess-v2.5-Qwen2-72B-gguff --hf-file tess-2.5-qwen-2-70b-q3_k_m.gguf -p "The meaning to life and the universe is"

Server

To start the llama.cpp server with this model, use the following command:

llama-server --hf-repo pabloce/Tess-v2.5-Qwen2-72B-gguff --hf-file tess-2.5-qwen-2-70b-q3_k_m.gguf -c 2048

Alternative Usage

You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repository.

Clone the llama.cpp repository from GitHub:

git clone https://github.com/ggerganov/llama.cpp

Navigate to the llama.cpp folder and build it with the LLAMA_CURL=1 flag. You can also include other hardware-specific flags (e.g., LLAMA_CUDA=1 for Nvidia GPUs on Linux):
```
cd llama.cpp && LLAMA_CURL=1 make
```

Run inference through the main binary:

./main --hf-repo pabloce/Tess-v2.5-Qwen2-72B-gguf --hf-file tess-2.5-qwen-2-70b-q3_k_m.gguf -p "The meaning to life and the universe is"

or start the server:

./server --hf-repo pabloce/Tess-v2.5-Qwen2-72B-gguf --hf-file tess-2.5-qwen-2-70b-q3_k_m.gguf -c 2048

Downloads last month: 26

GGUF

Model size

73B params

Architecture

qwen2

Hardware compatibility

3-bit

4-bit

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Model tree for pabloce/Tess-v2.5-Qwen2-72B-gguf

Base model

migtissera/Tess-v2.5-Qwen2-72B

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