Instructions to use bartowski/Replete-Coder-Qwen2-1.5b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/Replete-Coder-Qwen2-1.5b-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="bartowski/Replete-Coder-Qwen2-1.5b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bartowski/Replete-Coder-Qwen2-1.5b-GGUF", dtype="auto")

llama-cpp-python

How to use bartowski/Replete-Coder-Qwen2-1.5b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bartowski/Replete-Coder-Qwen2-1.5b-GGUF",
	filename="Replete-Coder-Qwen-1.5b-IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use bartowski/Replete-Coder-Qwen2-1.5b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/Replete-Coder-Qwen2-1.5b-GGUF: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 bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/Replete-Coder-Qwen2-1.5b-GGUF: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 bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/Replete-Coder-Qwen2-1.5b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/Replete-Coder-Qwen2-1.5b-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": "bartowski/Replete-Coder-Qwen2-1.5b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M

SGLang

How to use bartowski/Replete-Coder-Qwen2-1.5b-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 "bartowski/Replete-Coder-Qwen2-1.5b-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/Replete-Coder-Qwen2-1.5b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "bartowski/Replete-Coder-Qwen2-1.5b-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/Replete-Coder-Qwen2-1.5b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bartowski/Replete-Coder-Qwen2-1.5b-GGUF with Ollama:
```
ollama run hf.co/bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use bartowski/Replete-Coder-Qwen2-1.5b-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 bartowski/Replete-Coder-Qwen2-1.5b-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 bartowski/Replete-Coder-Qwen2-1.5b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/Replete-Coder-Qwen2-1.5b-GGUF to start chatting

Docker Model Runner
How to use bartowski/Replete-Coder-Qwen2-1.5b-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/Replete-Coder-Qwen2-1.5b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/Replete-Coder-Qwen2-1.5b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Replete-Coder-Qwen2-1.5b-GGUF-Q4_K_M

List all available models

lemonade list

Llamacpp imatrix Quantizations of Replete-Coder-Qwen-1.5b

Using llama.cpp release b3197 for quantization.

Original model: https://huggingface.co/Replete-AI/Replete-Coder-Qwen-1.5b

All quants made using imatrix option with dataset from here

Prompt format

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Description
Replete-Coder-Qwen-1.5b-Q8_0_L.gguf	Q8_0_L	1870.00MB	Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant.
Replete-Coder-Qwen-1.5b-Q8_0.gguf	Q8_0	1646.57MB	Extremely high quality, generally unneeded but max available quant.
Replete-Coder-Qwen-1.5b-Q6_K_L.gguf	Q6_K_L	1550MB	Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, recommended.
Replete-Coder-Qwen-1.5b-Q6_K.gguf	Q6_K	1272.73MB	Very high quality, near perfect, recommended.
Replete-Coder-Qwen-1.5b-Q5_K_L.gguf	Q5_K_L	1400MB	Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, recommended.
Replete-Coder-Qwen-1.5b-Q5_K_M.gguf	Q5_K_M	1125.04MB	High quality, recommended.
Replete-Coder-Qwen-1.5b-Q4_K_L.gguf	Q4_K_L	1260MB	Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, recommended.
Replete-Coder-Qwen-1.5b-Q4_K_M.gguf	Q4_K_M	986.04MB	Good quality, uses about 4.83 bits per weight, recommended.
Replete-Coder-Qwen-1.5b-IQ4_XS.gguf	IQ4_XS	895.72MB	Decent quality, smaller than Q4_K_S with similar performance, recommended.
Replete-Coder-Qwen-1.5b-Q3_K_XL.gguf	Q3_K_XL	1160MB	Experimental, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability.
Replete-Coder-Qwen-1.5b-Q3_K_L.gguf	Q3_K_L	880.16MB	Lower quality but usable, good for low RAM availability.

Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download bartowski/Replete-Coder-Qwen-1.5b-GGUF --include "Replete-Coder-Qwen-1.5b-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/Replete-Coder-Qwen-1.5b-GGUF --include "Replete-Coder-Qwen-1.5b-Q8_0.gguf/*" --local-dir Replete-Coder-Qwen-1.5b-Q8_0

You can either specify a new local-dir (Replete-Coder-Qwen-1.5b-Q8_0) or download them all in place (./)

Which file should I choose?

A great write up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

Downloads last month: 488

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for bartowski/Replete-Coder-Qwen2-1.5b-GGUF

Base model

Qwen/Qwen2-1.5B

Quantized

(22)

this model

Datasets used to train bartowski/Replete-Coder-Qwen2-1.5b-GGUF

Evaluation results

pass@1 on HumanEval
self-reported
normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard
multiple_choice_accuracy on TruthfulQA (0-shot)
validation set Open LLM Leaderboard
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard