gh-ai-runner 0.1.3

Serverless AI inference via GitHub Actions — no server required

Servai

Serverless AI inference via GitHub Actions. No server. No GPU. No infrastructure.

Run open-source LLMs directly through GitHub's free CI/CD runners — just a GitHub token and a prompt. servai handles everything else: repo creation, workflow setup, model downloading, caching, and output retrieval.

Built by Tanish Chauhan

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How it works

When you call ai_call():

1. Creates a private GitHub repo (once, automatically)
2. Commits a workflow + inference script into it
3. Dispatches a workflow_dispatch GitHub Actions run
4. The runner downloads and caches the model (GGUF quantized, ~0.6 GB)
5. Runs inference via llama-cpp-python
6. Uploads the output as an artifact
7. Downloads and returns the output to you as a string

No server is ever running between calls. Each call spins up a fresh GitHub Actions runner, runs inference, and shuts down.

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Install

pip install servai

Requirements:

• Python 3.9+
• A GitHub account with a personal access token (PAT)
• Token scopes needed: repo, workflow

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Getting a GitHub Token

You need a GitHub Personal Access Token (PAT) with repo and workflow scopes.

Classic token (recommended):

1. Go to github.com/settings/tokens
2. Click Generate new token > Generate new token (classic)
3. Give it a name (e.g. servai)
4. Set an expiration
5. Check these scopes:
   • `repo

from servai import ai_call

result = ai_call(
    github_token="ghp_...",
    prompt="explain recursion in simple terms",
)

print(result)

That's it. On first run, servai will:

• Create a repo called servai-runner on your GitHub account
• Set up the workflow automatically
• Download and cache TinyLlama 1.1B (~0.6 GB)

Subsequent calls reuse the cached repo and model.

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Examples

Basic question

from servai import ai_call

result = ai_call(
    github_token="ghp_...",
    prompt="what is the difference between a list and a tuple in Python?",
)
print(result)

Custom system prompt

result = ai_call(
    github_token="ghp_...",
    prompt="explain black holes",
    system="You are a physics professor. Be precise and use analogies.",
    model="llama",
    max_tokens=1024,
)
print(result)

Deterministic output (temperature=0)

result = ai_call(
    github_token="ghp_...",
    prompt="what is 144 divided by 12?",
    temperature=0.0,
    max_tokens=16,
)
print(result)  # always returns the same answer

Silent mode (no logs)

result = ai_call(
    github_token="ghp_...",
    prompt="summarize the theory of evolution",
    verbose=False,
)
print(result)

Large output

result = ai_call(
    github_token="ghp_...",
    prompt="write a detailed essay on the causes of World War I",
    max_tokens=2048,
    temperature=0.5,
    model="llama",
)
print(result)

Multiple calls in sequence

from servai import ai_call

TOKEN = "ghp_..."

questions = [
    "what is a neural network?",
    "what is gradient descent?",
    "what is backpropagation?",
]

for q in questions:
    answer = ai_call(github_token=TOKEN, prompt=q, verbose=False)
    print(f"Q: {q}\nA: {answer}\n")

Parallel calls (use separate repo per call)

import threading
from servai import ai_call

TOKEN = "ghp_..."
results = {}

def run(key, prompt, repo):
    results[key] = ai_call(
        github_token=TOKEN,
        prompt=prompt,
        repo_name=repo,
        verbose=False,
    )

t1 = threading.Thread(target=run, args=("q1", "explain DNA", "servai-repo-1"))
t2 = threading.Thread(target=run, args=("q2", "explain RNA", "servai-repo-2"))

t1.start(); t2.start()
t1.join();  t2.join()

print(results["q1"])
print(results["q2"])

Note: Parallel calls must use different repo_name values. Each repo has its own independent run queue, so calls never interfere with each other.

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Parameters

Parameter	Type	Default	Required	Description
github_token	str	—	Yes	GitHub PAT with repo and workflow scopes
prompt	str	—	Yes	The message or question to send to the model
model	str	"tinyllama"	No	Which model to use. See Models section below
system	str	"You are a helpful assistant."	No	System prompt that controls model behavior
max_tokens	int	512	No	Max tokens to generate. Hard limit: 4096
temperature	float	0.7	No	Randomness. 0.0 = deterministic, 2.0 = very creative
cache	bool	True	No	Cache model weights between runs. Strongly recommended
n_ctx	int	None	No	Context window size. Defaults to model built-in. Max: 8192
repo_name	str	"servai-runner"	No	GitHub repo to create or reuse for running inference
verbose	bool	True	No	Print step-by-step logs. Set False for silent mode

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Models

Key	Model	Size	Default Context	Max Safe Context
tinyllama	TinyLlama 1.1B Chat Q4_K_M	0.6 GB	2048 tokens	8192 tokens
llama	Llama 3.2 1B Instruct Q4_K_M	0.7 GB	4096 tokens	8192 tokens

Both models are quantized GGUF files hosted publicly on HuggingFace. No HuggingFace token required.

Which model should I use?

• tinyllama — faster, smaller, good for short factual answers and simple tasks
• llama — better instruction-following, better for longer structured outputs and reasoning

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Context and output size

The context window (n_ctx) is the total number of tokens the model can see at once — this includes your system prompt, your prompt, and the generated output combined.