Instructions to use npc-worldwide/TinyTimV1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use npc-worldwide/TinyTimV1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="npc-worldwide/TinyTimV1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("npc-worldwide/TinyTimV1")
model = AutoModelForCausalLM.from_pretrained("npc-worldwide/TinyTimV1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use npc-worldwide/TinyTimV1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="npc-worldwide/TinyTimV1",
	filename="ggml-model-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use npc-worldwide/TinyTimV1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf npc-worldwide/TinyTimV1:F16
# Run inference directly in the terminal:
llama-cli -hf npc-worldwide/TinyTimV1:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf npc-worldwide/TinyTimV1:F16
# Run inference directly in the terminal:
llama-cli -hf npc-worldwide/TinyTimV1:F16

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 npc-worldwide/TinyTimV1:F16
# Run inference directly in the terminal:
./llama-cli -hf npc-worldwide/TinyTimV1:F16

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 npc-worldwide/TinyTimV1:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf npc-worldwide/TinyTimV1:F16

Use Docker

docker model run hf.co/npc-worldwide/TinyTimV1:F16

LM Studio
Jan

vLLM

How to use npc-worldwide/TinyTimV1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "npc-worldwide/TinyTimV1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "npc-worldwide/TinyTimV1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/npc-worldwide/TinyTimV1:F16

SGLang

How to use npc-worldwide/TinyTimV1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "npc-worldwide/TinyTimV1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "npc-worldwide/TinyTimV1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "npc-worldwide/TinyTimV1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "npc-worldwide/TinyTimV1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use npc-worldwide/TinyTimV1 with Ollama:
```
ollama run hf.co/npc-worldwide/TinyTimV1:F16
```

Unsloth Studio new

How to use npc-worldwide/TinyTimV1 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 npc-worldwide/TinyTimV1 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 npc-worldwide/TinyTimV1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for npc-worldwide/TinyTimV1 to start chatting

Docker Model Runner
How to use npc-worldwide/TinyTimV1 with Docker Model Runner:
```
docker model run hf.co/npc-worldwide/TinyTimV1:F16
```

Lemonade

How to use npc-worldwide/TinyTimV1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull npc-worldwide/TinyTimV1:F16

Run and chat with the model

lemonade run user.TinyTimV1-F16

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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Check out the documentation for more information.

TinyTimV1: Fine-tuning TinyLlama on Finnegan's Wake

A project exploring the fine-tuning of TinyLlama-1.1B on James Joyce's Finnegan's Wake to generate Joyce-inspired text.

Overview

This project fine-tunes the TinyLlama-1.1B-Chat model on the complete text of James Joyce's Finnegan's Wake, creating a language model capable of generating text in Joyce's distinctive experimental style. The model learns to replicate the complex wordplay, neologisms, and stream-of-consciousness narrative techniques characteristic of Joyce's final work.

Files

process_wake.py - Preprocesses the raw text, removes page numbers, and splits into manageable chunks
fine_tune_joyce.py - Main training script using HuggingFace Transformers
text_gen.py - Text generation script for the fine-tuned model
finn_wake.txt - Complete text of Finnegan's Wake (1.51 MB)
finn_wake.csv - Processed dataset in CSV format
finn_wake_dataset/ - Tokenized dataset directory

Usage

1. Data Preprocessing

python process_wake.py

This removes page numbers and splits the text into 100-word chunks for training. 2. Fine-tuning

python fine_tune_joyce.py

Fine-tunes TinyLlama on the processed dataset for 3 epochs with CPU training. 3. Text Generation

python text_gen.py

Generates Joyce-inspired text using the fine-tuned model.

Model Details

Base Model: TinyLlama-1.1B-Chat-v1.0 Training Data: Finnegan's Wake (~1.5MB text) Training Parameters:

3 epochs Batch size: 1 Max sequence length: 128 tokens Temperature: 0.7 Top-k: 50, Top-p: 0.95

Example Output Input: "ae left to go to ireland and found a fairy" The model generates text continuing in Joyce's experimental style with invented words, Irish references, and complex linguistic play. Requirements transformers datasets pandas torch Installation bashpip install transformers datasets pandas torch Notes

Training was performed on CPU due to resource constraints Model checkpoints saved every 500 steps Resume training supported from checkpoints

Downloads last month: 155

Safetensors

Model size

1B params

Tensor type

F32

Model tree for npc-worldwide/TinyTimV1

Quantizations

2 models