Instructions to use sinj3d/resume-builder-coverletter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sinj3d/resume-builder-coverletter with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sinj3d/resume-builder-coverletter:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sinj3d/resume-builder-coverletter:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sinj3d/resume-builder-coverletter:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sinj3d/resume-builder-coverletter: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 sinj3d/resume-builder-coverletter:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sinj3d/resume-builder-coverletter: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 sinj3d/resume-builder-coverletter:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sinj3d/resume-builder-coverletter:Q4_K_M

Use Docker

docker model run hf.co/sinj3d/resume-builder-coverletter:Q4_K_M

LM Studio
Jan

vLLM

How to use sinj3d/resume-builder-coverletter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sinj3d/resume-builder-coverletter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sinj3d/resume-builder-coverletter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sinj3d/resume-builder-coverletter:Q4_K_M

Ollama
How to use sinj3d/resume-builder-coverletter with Ollama:
```
ollama run hf.co/sinj3d/resume-builder-coverletter:Q4_K_M
```

Unsloth Studio

How to use sinj3d/resume-builder-coverletter 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 sinj3d/resume-builder-coverletter 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 sinj3d/resume-builder-coverletter to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sinj3d/resume-builder-coverletter to start chatting

How to use sinj3d/resume-builder-coverletter with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sinj3d/resume-builder-coverletter:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sinj3d/resume-builder-coverletter:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use sinj3d/resume-builder-coverletter with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sinj3d/resume-builder-coverletter:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "sinj3d/resume-builder-coverletter:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use sinj3d/resume-builder-coverletter with Docker Model Runner:
```
docker model run hf.co/sinj3d/resume-builder-coverletter:Q4_K_M
```

Lemonade

How to use sinj3d/resume-builder-coverletter with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sinj3d/resume-builder-coverletter:Q4_K_M

Run and chat with the model

lemonade run user.resume-builder-coverletter-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use sinj3d/resume-builder-coverletter with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sinj3d/resume-builder-coverletter:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default sinj3d/resume-builder-coverletter:Q4_K_M

Run Hermes

hermes

Atomic Chat

resume-builder-coverletter (GGUF)

A fine-tune of Qwen2.5-3B-Instruct that writes job cover letters in the exact prompt format used by the resume-builder desktop app. It is conditioned on cover-letter templates — it follows whatever template appears in its prompt, so new templates work without retraining — and is grounded on retrieved résumé bullets (RAG) to avoid fabricating experience.

Built with Qwen.

Base model: Qwen/Qwen2.5-3B-Instruct
Format: GGUF, Q4_K_M quantization (~1.8 GB; ~3 GB RSS at runtime)
Runtime: CPU via llama.cpp — roughly 1–2 min per letter on an 8 GB machine
Prompt format: ChatML (the model's embedded chat template)

Usage

In the resume-builder app (recommended)

Open Settings → Local (GGUF) and click Download the tuned cover-letter model. The app fetches this file into its app-data directory and wires it up automatically — no manual paths.

Directly with llama.cpp

llama-cli -m coverletter-qwen2.5-3b-Q4_K_M.gguf -p "your ChatML prompt"

The app builds its own system/user prompt (RAG bullets + job description + optional template) and applies the model's chat template; see src-tauri/src/llm/prompt.rs for the exact shape.

Training

Distilled and QLoRA-fine-tuned entirely on synthetic, non-personal data — real public job descriptions paired with fictional candidate profiles, so the model generalizes at inference instead of memorizing anyone's résumé. Full pipeline: training/.

License

Non-commercial use only. This is a derivative of Qwen2.5-3B-Instruct and is distributed under the Qwen Research License Agreement (see LICENSE and NOTICE), which permits research and evaluation use only.

Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT, Copyright (c) Alibaba Cloud. All Rights Reserved.

Downloads last month: 24

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for sinj3d/resume-builder-coverletter

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(266)

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