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

caug37 commited on Aug 4, 2025

Commit

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@@ -1,9 +1,67 @@
-TinyTim
----
-license: llama2
----
-TinyTim is a version of TinyLlama that has been finetuned on text from James Joyce's Finnegan's Wake.
-Hope other Joyce fans find it useful or entertaining!

+# 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
+```bash
+python process_wake.py
+```
+This removes page numbers and splits the text into 100-word chunks for training.
+2. Fine-tuning
+```bash
+python fine_tune_joyce.py
+```
+Fine-tunes TinyLlama on the processed dataset for 3 epochs with CPU training.
+3. Text Generation
+```bash
+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