Instructions to use Gnayo/chronicle-llm-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gnayo/chronicle-llm-v0 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Gnayo/chronicle-llm-v0",
	filename="chronicle_v0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Gnayo/chronicle-llm-v0 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Gnayo/chronicle-llm-v0
# Run inference directly in the terminal:
llama-cli -hf Gnayo/chronicle-llm-v0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Gnayo/chronicle-llm-v0
# Run inference directly in the terminal:
llama-cli -hf Gnayo/chronicle-llm-v0

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 Gnayo/chronicle-llm-v0
# Run inference directly in the terminal:
./llama-cli -hf Gnayo/chronicle-llm-v0

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 Gnayo/chronicle-llm-v0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Gnayo/chronicle-llm-v0

Use Docker

docker model run hf.co/Gnayo/chronicle-llm-v0

LM Studio
Jan

vLLM

How to use Gnayo/chronicle-llm-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gnayo/chronicle-llm-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gnayo/chronicle-llm-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Gnayo/chronicle-llm-v0

Ollama
How to use Gnayo/chronicle-llm-v0 with Ollama:
```
ollama run hf.co/Gnayo/chronicle-llm-v0
```

Unsloth Studio new

How to use Gnayo/chronicle-llm-v0 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 Gnayo/chronicle-llm-v0 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 Gnayo/chronicle-llm-v0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Gnayo/chronicle-llm-v0 to start chatting

Docker Model Runner
How to use Gnayo/chronicle-llm-v0 with Docker Model Runner:
```
docker model run hf.co/Gnayo/chronicle-llm-v0
```

Lemonade

How to use Gnayo/chronicle-llm-v0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Gnayo/chronicle-llm-v0

Run and chat with the model

lemonade run user.chronicle-llm-v0-{{QUANT_TAG}}

List all available models

lemonade list

Chronicle LLM v0 🇦🇺

A GPT-style language model trained from scratch on Australian texts from 1850-1950. No fine-tuning. No modern weights. Built entirely from historical Australian writing.

Model Details

Architecture: GPT-2 decoder-only transformer
Parameters: 30M
Training data: 141 verified Australian texts, 55MB cleaned, ~14M tokens
Training steps: 20,000
Final train loss: 2.81
Final val loss: 4.68

Files

model.safetensors - model weights (HuggingFace format)
chronicle_v0.gguf - GGUF format for LM Studio and llama.cpp

Usage

Load in LM Studio using the GGUF file, or via API:

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model = GPT2LMHeadModel.from_pretrained("Gnayo/chronicle-llm-v0")
tokenizer = GPT2Tokenizer.from_pretrained("Gnayo/chronicle-llm-v0")

GitHub

Full training code and documentation: https://github.com/ravipatib/ChronicleLLM

Downloads last month: 1,314

Safetensors

Model size

30.1M params

Tensor type

F32