Instructions to use awels/threadyLLM-3b-128k-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use awels/threadyLLM-3b-128k-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="awels/threadyLLM-3b-128k-gguf",
	filename="threadyLLM-phi3-128k-3b-v0.1.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 awels/threadyLLM-3b-128k-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf awels/threadyLLM-3b-128k-gguf
# Run inference directly in the terminal:
llama-cli -hf awels/threadyLLM-3b-128k-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf awels/threadyLLM-3b-128k-gguf
# Run inference directly in the terminal:
llama-cli -hf awels/threadyLLM-3b-128k-gguf

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 awels/threadyLLM-3b-128k-gguf
# Run inference directly in the terminal:
./llama-cli -hf awels/threadyLLM-3b-128k-gguf

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 awels/threadyLLM-3b-128k-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf awels/threadyLLM-3b-128k-gguf

Use Docker

docker model run hf.co/awels/threadyLLM-3b-128k-gguf

LM Studio
Jan
Ollama
How to use awels/threadyLLM-3b-128k-gguf with Ollama:
```
ollama run hf.co/awels/threadyLLM-3b-128k-gguf
```

Unsloth Studio new

How to use awels/threadyLLM-3b-128k-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 awels/threadyLLM-3b-128k-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 awels/threadyLLM-3b-128k-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for awels/threadyLLM-3b-128k-gguf to start chatting

Docker Model Runner
How to use awels/threadyLLM-3b-128k-gguf with Docker Model Runner:
```
docker model run hf.co/awels/threadyLLM-3b-128k-gguf
```

Lemonade

How to use awels/threadyLLM-3b-128k-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull awels/threadyLLM-3b-128k-gguf

Run and chat with the model

lemonade run user.threadyLLM-3b-128k-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Thready Model Card

Model Details

Model Name: Thready

Model Type: Transformer-based leveraging Microsoft Phi 3b 128k tokens

Publisher: Awels Engineering

License: MIT

Model Description: Thready is a sophisticated model designed to help as an AI agent focusing on the Red Hat Openshift Virtualization solution. It leverages advanced machine learning techniques to provide efficient and accurate solutions. It has been trained on the full docments corpus of OCP Virt 4.16.

Dataset

Dataset Name: awels/ocpvirt_admin_dataset

Dataset Source: Hugging Face Datasets

Dataset License: MIT

Dataset Description: The dataset used to train Thready consists of all the public documents available on Red Hat Openshift Virtualization. This dataset is curated to ensure a comprehensive representation of typical administrative scenarios encountered in Openshift Virtualization.

Training Details

Training Data: The training data includes 70,000 Questions and Answers generated by the Bonito LLM. The dataset is split into 3 sets of data (training, test and validation) to ensure robust model performance.

Training Procedure: Thready was trained using supervised learning with cross-entropy loss and the Adam optimizer. The training involved 1 epoch, a batch size of 4, a learning rate of 5.0e-06, and a cosine learning rate scheduler with gradient checkpointing for memory efficiency.

Hardware: The model was trained on a single NVIDIA RTX 4090 graphic card.

Framework: The training was conducted using PyTorch.

Evaluation

Evaluation Metrics: Thready was evaluated on the training dataset:

epoch = 1.0 total_flos = 74851620GF train_loss = 2.6706 train_runtime = 0:41:52.37 train_samples_per_second = 22.229 train_steps_per_second = 5.557

Performance: The model achieved the following results on the evaluation dataset:

epoch = 1.0 eval_loss = 2.2243 eval_runtime = 0:02:21.35 eval_samples = 11191 eval_samples_per_second = 97.867 eval_steps_per_second = 24.47

Intended Use

Primary Use Case: Thready is intended to be used locally in an agent swarm to colleborate together to solve Red Hat Openshift Virtualization related problems.

Limitations: While Thready is highly effective, it may have limitations due to the model size. An 8b model based on Llama 3 is used internally at Awels Engineering.

Downloads last month: 8

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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