Instructions to use c7s89r/HazLLM-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use c7s89r/HazLLM-1.5B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="c7s89r/HazLLM-1.5B",
	filename="HazLLM-v1-1.5B-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use c7s89r/HazLLM-1.5B 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 c7s89r/HazLLM-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf c7s89r/HazLLM-1.5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf c7s89r/HazLLM-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf c7s89r/HazLLM-1.5B: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 c7s89r/HazLLM-1.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf c7s89r/HazLLM-1.5B: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 c7s89r/HazLLM-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf c7s89r/HazLLM-1.5B:Q4_K_M

Use Docker

docker model run hf.co/c7s89r/HazLLM-1.5B:Q4_K_M

LM Studio
Jan

vLLM

How to use c7s89r/HazLLM-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "c7s89r/HazLLM-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "c7s89r/HazLLM-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/c7s89r/HazLLM-1.5B:Q4_K_M

Ollama
How to use c7s89r/HazLLM-1.5B with Ollama:
```
ollama run hf.co/c7s89r/HazLLM-1.5B:Q4_K_M
```

Unsloth Studio

How to use c7s89r/HazLLM-1.5B 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 c7s89r/HazLLM-1.5B 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 c7s89r/HazLLM-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for c7s89r/HazLLM-1.5B to start chatting

How to use c7s89r/HazLLM-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf c7s89r/HazLLM-1.5B: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": "c7s89r/HazLLM-1.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use c7s89r/HazLLM-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf c7s89r/HazLLM-1.5B: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 c7s89r/HazLLM-1.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use c7s89r/HazLLM-1.5B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf c7s89r/HazLLM-1.5B: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 "c7s89r/HazLLM-1.5B: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 c7s89r/HazLLM-1.5B with Docker Model Runner:
```
docker model run hf.co/c7s89r/HazLLM-1.5B:Q4_K_M
```

Lemonade

How to use c7s89r/HazLLM-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull c7s89r/HazLLM-1.5B:Q4_K_M

Run and chat with the model

lemonade run user.HazLLM-1.5B-Q4_K_M

List all available models

lemonade list

HazLLM 1.5B

HazLLM is a highly specialized, locally fine-tuned 1.5B parameter language model engineered explicitly for Roblox Luau development and modern web development (HTML, CSS, JS).

Built on top of Qwen2.5-Coder-1.5B-Instruct, this model was surgically tuned to eliminate the repetition degeneration bug commonly found in smaller models. It delivers concise, expert-level code without hallucinating or looping endlessly.

Training Statistics

Base Model: unsloth/Qwen2.5-Coder-1.5B-Instruct
Dataset: 6,636 highly-curated instruction pairs
Focus Areas: Roblox Luau, Modern Web Dev, Anti-Repetition logic
Final Loss: 0.5201 (Dropped from 1.325)
Hardware: Trained locally on a single NVIDIA RTX 4060 (8GB VRAM)
Peak VRAM Usage: 7.82 GB (97.7% absolute optimization limit)
LoRA Rank: 64 (Massive capacity for complex API retention)
Precision: bfloat16
Optimization Framework: Unsloth Engine

Architecture and Methodology

The core problem with 1.5B parameter models is their tendency to fall into repetition loops or output generic, unoptimized code. HazLLM solves this through a highly specialized dataset and aggressive hyperparameter tuning.

The training data consists of 6,636 manually structured JSONL objects. We explicitly trained the model on modern Luau paradigms, such as TweenService, DataStoreService, strict type-checking, and responsive GUI constraints. For web development, the model was fed modern semantic HTML5, CSS Grid/Flexbox architectures, and vanilla ES6+ JavaScript.

To kill the repetition bug, the dataset included dedicated "anti-repetition" conversations where the assistant was explicitly rewarded for stopping its generation cleanly. During inference, we run a repetition penalty of 1.2 paired with a no_repeat_ngram_size of 4, ensuring absolute conciseness.

Training Pipeline

The training pipeline bypassed standard web-UI fine-tuning limits by using custom Python scripts built directly around the unsloth library. The trainer successfully bypassed PyTorch sub-byte bugs and HuggingFace pickling crashes to deliver a seamless, ultra-optimized 2.5 hour training run on consumer hardware.

The optimizer used was adamw_8bit with a learning rate of 3e-4 over 2 complete epochs, allowing the model to quickly converge to a loss of 0.5201 without overfitting.

Capabilities

Roblox Luau Expert: Fully understands TweenService, ScreenGui, DataStoreService, and modern Luau type-checking.
Web Developer: Writes semantic HTML5, CSS3 (Flexbox/Grid), and vanilla JS efficiently.
Strictly Concise: Trained with custom system prompts and data specifically designed to force the AI to stop generating once the correct answer is given. No endless looping.

Usage

You can download the GGUF model directly from this repository to run in Ollama, LM Studio, or llama.cpp. Alternatively, you can run the raw 16-bit weights using standard HuggingFace pipelines.

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("c7s89r/HazLLM-1.5B")
model = AutoModelForCausalLM.from_pretrained("c7s89r/HazLLM-1.5B", device_map="auto")

prompt = "Write a Roblox script to fade a GUI in using TweenService."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))