Instructions to use Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF", dtype="auto")

llama-cpp-python

How to use Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF",
	filename="Starcoder2-15B-model-q2-k.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16

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 Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16

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 Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16

Use Docker

docker model run hf.co/Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16

LM Studio
Jan

vLLM

How to use Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16

SGLang

How to use Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF with Ollama:
```
ollama run hf.co/Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16
```

Unsloth Studio new

How to use Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF 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 Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF 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 Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF to start chatting

Docker Model Runner
How to use Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16
```

Lemonade

How to use Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kquant03/TechxGenus-starcoder2-15b-instruct-GGUF:F16

Run and chat with the model

lemonade run user.TechxGenus-starcoder2-15b-instruct-GGUF-F16

List all available models

lemonade list

starcoder2-instruct

starcoder2-instruct (not my model, I just quantized it)

We've 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).

Usage

Here give some examples of how to use our model:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
PROMPT = """### Instruction
{instruction}
### Response
"""
instruction = <Your code instruction here>
prompt = PROMPT.format(instruction=instruction)
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/starcoder2-15b-instruct")
model = AutoModelForCausalLM.from_pretrained(
    "TechxGenus/starcoder2-15b-instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=2048)
print(tokenizer.decode(outputs[0]))

With text-generation pipeline:

from transformers import pipeline
import torch
PROMPT = """### Instruction
{instruction}
### Response
"""
instruction = <Your code instruction here>
prompt = PROMPT.format(instruction=instruction)
generator = pipeline(
    model="TechxGenus/starcoder2-15b-instruct",
    task="text-generation",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
result = generator(prompt, max_length=2048)
print(result[0]["generated_text"])

Note

Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding. It has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.

Downloads last month: 149

GGUF

Model size

16B params

Architecture

starcoder2

Hardware compatibility

16-bit

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