Instructions to use dranger003/starcoder2-15b-instruct-iMat.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 dranger003/starcoder2-15b-instruct-iMat.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 dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS
# Run inference directly in the terminal:
llama cli -hf dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS
# Run inference directly in the terminal:
llama cli -hf dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS

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 dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS
# Run inference directly in the terminal:
./llama-cli -hf dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS

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 dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS

Use Docker

docker model run hf.co/dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS

LM Studio
Jan

vLLM

How to use dranger003/starcoder2-15b-instruct-iMat.GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dranger003/starcoder2-15b-instruct-iMat.GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dranger003/starcoder2-15b-instruct-iMat.GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS

Ollama
How to use dranger003/starcoder2-15b-instruct-iMat.GGUF with Ollama:
```
ollama run hf.co/dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS
```
Unsloth Desktop
Docker Model Runner
How to use dranger003/starcoder2-15b-instruct-iMat.GGUF with Docker Model Runner:
```
docker model run hf.co/dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS
```

Lemonade

How to use dranger003/starcoder2-15b-instruct-iMat.GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dranger003/starcoder2-15b-instruct-iMat.GGUF:IQ3_XS

Run and chat with the model

lemonade run user.starcoder2-15b-instruct-iMat.GGUF-IQ3_XS

List all available models

lemonade list

Atomic Chat

NOTE: You will need a recent build of llama.cpp to run these quants (i.e. at least commit 494c870).

GGUF importance matrix (imatrix) quants for https://huggingface.co/TechxGenus/starcoder2-15b-instruct

The importance matrix was trained for ~50K tokens (105 batches of 512 tokens) using a general purpose imatrix calibration dataset.
The imatrix is being used on the K-quants as well.

Fine-tuned starcoder2-15b with an additional 0.7 billion high-quality, code-related tokens for 3 epochs. We used DeepSpeed ZeRO 3 and Flash Attention 2 to accelerate the training process. It achieves 77.4 pass@1 on HumanEval-Python. This model operates using the Alpaca instruction format (excluding the system prompt).

Layers	Context	Template
40	16384	### Instruction {instruction} ### Response {response}

Downloads last month: 122

GGUF

Model size

16B params

Architecture

starcoder2

Hardware compatibility

3-bit

4-bit

6-bit

8-bit

Model tree for dranger003/starcoder2-15b-instruct-iMat.GGUF

Base model

TechxGenus/starcoder2-15b-instruct

Quantized

(3)

this model