Instructions to use strykes/tiny-giant-30k-q4_k_m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use strykes/tiny-giant-30k-q4_k_m with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="strykes/tiny-giant-30k-q4_k_m",
	filename="tiny-giant-30k-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 strykes/tiny-giant-30k-q4_k_m 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 strykes/tiny-giant-30k-q4_k_m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf strykes/tiny-giant-30k-q4_k_m:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf strykes/tiny-giant-30k-q4_k_m:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf strykes/tiny-giant-30k-q4_k_m: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 strykes/tiny-giant-30k-q4_k_m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf strykes/tiny-giant-30k-q4_k_m: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 strykes/tiny-giant-30k-q4_k_m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf strykes/tiny-giant-30k-q4_k_m:Q4_K_M

Use Docker

docker model run hf.co/strykes/tiny-giant-30k-q4_k_m:Q4_K_M

LM Studio
Jan

vLLM

How to use strykes/tiny-giant-30k-q4_k_m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "strykes/tiny-giant-30k-q4_k_m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "strykes/tiny-giant-30k-q4_k_m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/strykes/tiny-giant-30k-q4_k_m:Q4_K_M

Ollama
How to use strykes/tiny-giant-30k-q4_k_m with Ollama:
```
ollama run hf.co/strykes/tiny-giant-30k-q4_k_m:Q4_K_M
```

Unsloth Studio

How to use strykes/tiny-giant-30k-q4_k_m 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 strykes/tiny-giant-30k-q4_k_m 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 strykes/tiny-giant-30k-q4_k_m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for strykes/tiny-giant-30k-q4_k_m to start chatting

How to use strykes/tiny-giant-30k-q4_k_m with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf strykes/tiny-giant-30k-q4_k_m: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": "strykes/tiny-giant-30k-q4_k_m:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use strykes/tiny-giant-30k-q4_k_m with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf strykes/tiny-giant-30k-q4_k_m: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 strykes/tiny-giant-30k-q4_k_m:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use strykes/tiny-giant-30k-q4_k_m with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf strykes/tiny-giant-30k-q4_k_m: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 "strykes/tiny-giant-30k-q4_k_m: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 strykes/tiny-giant-30k-q4_k_m with Docker Model Runner:
```
docker model run hf.co/strykes/tiny-giant-30k-q4_k_m:Q4_K_M
```

Lemonade

How to use strykes/tiny-giant-30k-q4_k_m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull strykes/tiny-giant-30k-q4_k_m:Q4_K_M

Run and chat with the model

lemonade run user.tiny-giant-30k-q4_k_m-Q4_K_M

List all available models

lemonade list

Tiny-Giant 30k — Q4_K_M

Full fine-tune of Qwen2.5-Coder-1.5B-Instruct on ~30k execution-verified Tiny-Giant agentic coding samples, quantized to Q4_K_M for llama.cpp.


Base	Qwen2.5-Coder-1.5B-Instruct
Params	1.5B (Q4_K_M ~940 MB)
Training	Full SFT, 2 epochs, ~29.5k train / 500 val
Final train loss	0.241
Decoding (public benchmarks)	greedy, EvalPlus / SWE-bench Pro scaffold

Benchmark results (Q4_K_M)

Benchmark	Score	Protocol
HumanEval pass@1	66.5%	EvalPlus, greedy, llama-server CUDA
HumanEval+ pass@1	62.2%	EvalPlus, greedy
SWE-bench Pro resolved	pending	SWE-agent + local Docker eval

Raw metrics: evals/tiny-giant-30k-Q4KM-30k.json

Comparison vs published baselines

Side-by-side with Qwen2.5-Coder, DeepSeek-Coder, StarCoder2, and frontier SWE-bench Pro numbers (sources in published_baselines.json).

Model comparison card

Metric	tiny-giant-30k-Q4_K_M.gguf [30k]	Qwen2.5-Coder-1.5B-Instruct (published)	Qwen2.5-Coder-7B-Instruct (published)	DeepSeek-Coder-1.3B-Instruct (published)	StarCoder2-3B (published)	Claude Sonnet 4.5 (SWE-bench Pro) (published)	GPT-4o (SWE-bench Pro) (published)
HumanEval (pass@1)	66.5	70.7	88.4	65.2	35.4	—	—
HumanEval+ (pass@1)	62.2	66.5	84.8	—	—	—	—
MBPP (pass@1)	—	69.2	83.5	65	46.4	—	—
MBPP+ (pass@1)	—	59.4	70.9	—	—	—	—
SWE-bench Pro (resolved %)	—	—	—	—	—	43	23

Reading rules: internal Tiny-Giant metrics exist only for our runs (published models were never evaluated on our held-out tests — those cells are honestly blank). Public benchmark cells for our models must come from greedy decoding via EvalPlus (see EVALUATE_MODELS.md). Published numbers are copied from official model cards/tech reports — sources in published_baselines.json. Disclose quant level and parameter count when sharing this card; a Q4_K_M 1.5B competing within points of larger bf16 models IS the headline, hiding the size difference is not.

Usage (llama-server)

llama-server -m tiny-giant-30k-Q4_K_M.gguf --port 8088 -ngl 99

License

Apache-2.0 (inherits from Qwen2.5-Coder-1.5B-Instruct).

Downloads last month: 30

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for strykes/tiny-giant-30k-q4_k_m

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Quantized

(135)

this model