Instructions to use jeeva1398/eventa-1.5b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jeeva1398/eventa-1.5b-gguf 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 jeeva1398/eventa-1.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jeeva1398/eventa-1.5b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jeeva1398/eventa-1.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jeeva1398/eventa-1.5b-gguf: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 jeeva1398/eventa-1.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf jeeva1398/eventa-1.5b-gguf:Q4_K_M

Use Docker

docker model run hf.co/jeeva1398/eventa-1.5b-gguf:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use jeeva1398/eventa-1.5b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeeva1398/eventa-1.5b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeeva1398/eventa-1.5b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jeeva1398/eventa-1.5b-gguf:Q4_K_M

Ollama
How to use jeeva1398/eventa-1.5b-gguf with Ollama:
```
ollama run hf.co/jeeva1398/eventa-1.5b-gguf:Q4_K_M
```
Unsloth Desktop

How to use jeeva1398/eventa-1.5b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jeeva1398/eventa-1.5b-gguf: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": "jeeva1398/eventa-1.5b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jeeva1398/eventa-1.5b-gguf with Docker Model Runner:
```
docker model run hf.co/jeeva1398/eventa-1.5b-gguf:Q4_K_M
```

Lemonade

How to use jeeva1398/eventa-1.5b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jeeva1398/eventa-1.5b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.eventa-1.5b-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use jeeva1398/eventa-1.5b-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jeeva1398/eventa-1.5b-gguf: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 jeeva1398/eventa-1.5b-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jeeva1398/eventa-1.5b-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jeeva1398/eventa-1.5b-gguf: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 "jeeva1398/eventa-1.5b-gguf: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"

eventa-1.5b (GGUF)

A small, offline assistant model for Node.js developers. It is a LoRA fine-tune of Qwen2.5-Coder-1.5B-Instruct, quantized to Q4_K_M (about 1 GB). It runs on a laptop CPU.

It is trained for the three tasks of the eventa CLI:

explain: a Node.js stack trace or TypeScript compile error plus the failing source lines → Cause / Fix / Prevent (Node, Express, NestJS, Prisma)
review: a git diff plus static-check hints → - [high|medium|low] line N: problem → fix, or No issues found.
deps: npm audit / outdated / unused / missing facts → a prioritized action plan that keeps the computed fix commands exactly

Use it

With the CLI (recommended). The CLI builds the right prompts for you:

npx @jeeva1398/eventa model use eventa-1.5b
node app.js 2>&1 | npx @jeeva1398/eventa explain
npx @jeeva1398/eventa review
npx @jeeva1398/eventa deps

In GitHub Actions. It reviews every pull request on the runner and posts one comment: uses: Jeeva1398/eventa@v0.2.1. See the repo README.

With Ollama:

ollama run hf.co/jeeva1398/eventa-1.5b-gguf

With llama.cpp:

llama-cli -hf jeeva1398/eventa-1.5b-gguf -sys "You are Eventa, an expert Node.js engineer."

Prompt format

It uses the Qwen ChatML template with this system prompt:

You are Eventa, an expert Node.js engineer. Answer concisely and accurately. Prefer modern Node.js (ESM, async/await, node: built-ins). Use markdown and fenced code blocks.

The user message is a task instruction followed by the context (error output and source, a diff, or dependency facts). For the exact templates, see packages/cli/src/prompts/ in the repo.

Training

Method: Unsloth QLoRA (r=16, alpha=16, all attention and MLP projections), 2 epochs, lr 2e-4, loss on responses only, run on a free T4 GPU.
Data: about 740 examples (68 crash types including TypeScript compile errors, NestJS and Prisma; 74 review scenarios: clean diffs, bugs that static checks flag, and bugs only careful reading finds) generated by training/src/build-dataset.ts:
- real Node.js programs that crash, run and parsed with the CLI's stack-trace parser, with hand-written causes and fixes
- annotated before/after diffs, including clean diffs where false-positive hints must be dismissed
- dependency reports built from real advisories and documented major-version breaking changes
Held out: whole scenarios are kept for evaluation.

Evaluation

54 held-out examples from scenarios never seen in training (npm run eval -w eventa-training). Both models were run through Ollama on CPU with the CLI's prompts.

Metric	Qwen2.5-Coder-1.5B (base)	eventa-1.5b
Explain: Cause/Fix/Prevent format	100%	100%
Explain: key facts mentioned	75%	68%
Review: real issues found	100%	100%
Review: precision (flagged lines that are real issues)	48%	100%
Review: correct severity	50%	63%
Review: clean diff → "No issues found."	0%	100%
Deps: exact fix commands	100%	100%
Deps: no invented versions	93%	100%
Avg seconds per answer (CPU)	13.6	5.9

The base model flags almost every line, so its 100% recall is mostly noise. eventa-1.5b reports only real problems and is about 2× faster because its answers are shorter. Explain quality on unseen error types is slightly below the base model and is the focus of the next data round.

Limitations

A 1.5B model can still be wrong or vague. Use its answers as a strong hint and verify fixes.
It is focused on Node.js / JavaScript / TypeScript backend code, and is weaker on browser and framework-specific frontend issues.
The deps advice relies on facts that the CLI computes. Used without the CLI, it can invent versions.