Instructions to use tanya8997/openwork-understudy-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tanya8997/openwork-understudy-0.5b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tanya8997/openwork-understudy-0.5b",
	filename="understudy-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tanya8997/openwork-understudy-0.5b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tanya8997/openwork-understudy-0.5b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf tanya8997/openwork-understudy-0.5b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tanya8997/openwork-understudy-0.5b:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf tanya8997/openwork-understudy-0.5b:Q4_K_M

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 tanya8997/openwork-understudy-0.5b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tanya8997/openwork-understudy-0.5b:Q4_K_M

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 tanya8997/openwork-understudy-0.5b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tanya8997/openwork-understudy-0.5b:Q4_K_M

Use Docker

docker model run hf.co/tanya8997/openwork-understudy-0.5b:Q4_K_M

LM Studio
Jan
Ollama
How to use tanya8997/openwork-understudy-0.5b with Ollama:
```
ollama run hf.co/tanya8997/openwork-understudy-0.5b:Q4_K_M
```

Unsloth Studio

How to use tanya8997/openwork-understudy-0.5b 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 tanya8997/openwork-understudy-0.5b 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 tanya8997/openwork-understudy-0.5b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tanya8997/openwork-understudy-0.5b to start chatting

Atomic Chat new
Docker Model Runner
How to use tanya8997/openwork-understudy-0.5b with Docker Model Runner:
```
docker model run hf.co/tanya8997/openwork-understudy-0.5b:Q4_K_M
```

Lemonade

How to use tanya8997/openwork-understudy-0.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tanya8997/openwork-understudy-0.5b:Q4_K_M

Run and chat with the model

lemonade run user.openwork-understudy-0.5b-Q4_K_M

List all available models

lemonade list

The Understudy 🎭 — openwork-understudy-0.5b

Fine-tuned lyricist for Open to Work: The Musical (Build Small Hackathon, Track 2 "Thousand Token Wood").

Writes short parody songs sung by a job candidate about wanting one specific job, conditioned on a Sendability level (1–10): 1 = polished enough to genuinely attach to an application, 10 = full unhinged parody. Outputs ACE-Step-ready [verse]/[chorus] structure with a GENRE:/TITLE:/LYRICS: header.

Training

Base: openbmb/MiniCPM4-0.5B (OpenBMB) — small enough that the whole "Tiny Mode" pipeline (this + ACE-Step 2B turbo) stays under ~3B parameters.
Data: 1,500 synthetic examples written by gpt-oss-20b: 60 fictional careers × 5 genres × sendability levels {1,3,5,7,10}, validated for format and zone calibration.
Method: LoRA (r=16, α=32, all-linear), 3 epochs, lr 2e-4 cosine, bf16, TRL SFTTrainer on Modal (A100, ~6 min). Eval loss 0.718, token accuracy 86%.
Merged weights in HF format + understudy-Q4_K_M.gguf for llama.cpp.

Prompt format

System prompt (condensed, CPU-friendly) + user message with sendability level, genre, job description, and resume. See the Space repo src/prompts.py (UNDERSTUDY_SYSTEM).

Role in the app

Instant slider-drag lyric previews on CPU via llama.cpp; the headline writer (gpt-oss-20b) composes final songs. Trained for comedy through specificity — it rhymes the bullet points of your resume.

Limitations

0.5B parameters of theater kid. Occasionally drops rhyme schemes, miscounts meter, or commits to a pun beyond reason. Trained only on English. Not a career advisor.