Instructions to use eramax/Tess-XS-v1-3-yarn-128K-q8-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eramax/Tess-XS-v1-3-yarn-128K-q8-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="eramax/Tess-XS-v1-3-yarn-128K-q8-gguf",
	filename="Tess-XS-v1-3-yarn-128K-q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use eramax/Tess-XS-v1-3-yarn-128K-q8-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0

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 eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0

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 eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0

Use Docker

docker model run hf.co/eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0

LM Studio
Jan
Ollama
How to use eramax/Tess-XS-v1-3-yarn-128K-q8-gguf with Ollama:
```
ollama run hf.co/eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0
```

Unsloth Studio

How to use eramax/Tess-XS-v1-3-yarn-128K-q8-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 eramax/Tess-XS-v1-3-yarn-128K-q8-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 eramax/Tess-XS-v1-3-yarn-128K-q8-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for eramax/Tess-XS-v1-3-yarn-128K-q8-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use eramax/Tess-XS-v1-3-yarn-128K-q8-gguf with Docker Model Runner:
```
docker model run hf.co/eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0
```

Lemonade

How to use eramax/Tess-XS-v1-3-yarn-128K-q8-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull eramax/Tess-XS-v1-3-yarn-128K-q8-gguf:Q8_0

Run and chat with the model

lemonade run user.Tess-XS-v1-3-yarn-128K-q8-gguf-Q8_0

List all available models

lemonade list

Q8-GGUF for migtissera/Tess-XS-v1-3-yarn-128K

Note:

This version is the stable release. The issues that were present in versions 1.0, 1.1 and 1.2 all have been rectified. Thank you for your patience while R&D was conducted. Enjoy!

This model have been tested on context length up to 16K. Model produced slight repetition around 16K context length. I recommend testing the model to your usecase and limiting the context length.

Here's my learnings going from Tess-v1.0 to Tess-v1.3: https://migel.substack.com/p/learnings-from-training-tess

Tess

Tess, short for Tessoro/Tessoso, is a general purpose Large Language Model series. Tess-XS-v1.3 was trained on the Nous Research Mistral-7B-yarn-128K base.

Prompt Format:

SYSTEM: <ANY SYSTEM CONTEXT>
USER: 
ASSISTANT:

Downloads last month: 3

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

8-bit

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