Text Generation
Safetensors
GGUF
English
qwen2
qwen
qwen2.5
json
structured-outputs
llama-cpp
conversational
Instructions to use rishabh-rishuu/Unpaid-JSON-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use rishabh-rishuu/Unpaid-JSON-Generator 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 rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M # Run inference directly in the terminal: llama cli -hf rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M # Run inference directly in the terminal: llama cli -hf rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M
Use Docker
docker model run hf.co/rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use rishabh-rishuu/Unpaid-JSON-Generator with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rishabh-rishuu/Unpaid-JSON-Generator" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rishabh-rishuu/Unpaid-JSON-Generator", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M
- Ollama
How to use rishabh-rishuu/Unpaid-JSON-Generator with Ollama:
ollama run hf.co/rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M
- Unsloth Desktop
- Pi
How to use rishabh-rishuu/Unpaid-JSON-Generator with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use rishabh-rishuu/Unpaid-JSON-Generator with Docker Model Runner:
docker model run hf.co/rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M
- Lemonade
How to use rishabh-rishuu/Unpaid-JSON-Generator with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M
Run and chat with the model
lemonade run user.Unpaid-JSON-Generator-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use rishabh-rishuu/Unpaid-JSON-Generator with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rishabh-rishuu/Unpaid-JSON-Generator: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 rishabh-rishuu/Unpaid-JSON-Generator:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use rishabh-rishuu/Unpaid-JSON-Generator with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf rishabh-rishuu/Unpaid-JSON-Generator: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 "rishabh-rishuu/Unpaid-JSON-Generator: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"
File size: 2,318 Bytes
075d97c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | ---
language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B
tags:
- qwen
- qwen2.5
- json
- structured-outputs
- gguf
- llama-cpp
pipeline_tag: text-generation
---
# Unpaid JSON Generator (Qwen-1.5B)
{ }
{ "status": "no_yap" }
[ ]
### What is this?
A lobotomized, hyper-focused version of `Qwen2.5-Coder-1.5B` stripped of its desire to be polite, conversational, or helpful.
Its sole reason for existence is to look at unstructured garbage (messy CSVs, server crash dumps, stream-of-consciousness notes) and spit out RFC-compliant, syntax-valid JSON objects without emitting so much as a single *"Sure! Here is the JSON you requested:"*.
If you try to have a heart-to-heart with it, it will probably return `{"error": "talk_to_a_therapist_not_a_json_parser"}`.
---
### Core Philosophy
* **Chatter Level:** 0.00%
* **First Token:** Guaranteed `{` or `[`
* **Markdown Fences:** Completely eradicated. Zero markdown code fences to `strip()` out of your downstream backend.
* **Typing:** Actually knows the difference between `42` (number), `"42"` (string), and `true` (boolean). Doesn't wrap everything in quotes like a terrified intern.
---
### Key Specs
| Attribute | Spec |
| :--- | :--- |
| **Base Model** | `Qwen/Qwen2.5-Coder-1.5B` |
| **Parameters** | 1,543,714,816 (~1.54B) |
| **Training Recipe** | Full BF16 SFT on 15,000 AST-verified Schema -> JSON pairs |
| **Prompt Masking** | Aggressive `-100` cross-entropy masking on all conversational tags |
| **Context Window** | 1,024 - 2,048 tokens |
| **Quantized Footprint** | ~980 MB (`Q4_K_M` GGUF) |
| **Speed** | ~14-55 tok/s on CPU; 120+ tok/s on modern silicon |
---
### How to Prompt It (ChatML)
Don't overcomplicate it. Give it data, tell it what keys to hit, and let it serialize:
<|im_start|>system
You are a strict JSON generator. Respond ONLY with valid JSON.<|im_end|>
<|im_start|>user
station_id,city,date,temp_max,temp_min,precipitation_mm,humidity_pct,wind_speed_kmh,condition
STN001,Seattle,2026-11-01,12.8,5.0,0.0,76,4.7,drizzle<|im_end|>
<|im_start|>assistant
{"station_id": "STN001", "city": "Seattle", "date": "2026-11-01", "temp_max": 12.8, "temp_min": 5.0, "precipitation_mm": 0, "humidity_pct": 76, "wind_speed_kmh": 4.7, "condition": "Drizzle"}<|im_end|>
|