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 Settings

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

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

Atomic Chat new
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

bartowski commited on Jun 23, 2024

Commit

af129e3

verified ·

1 Parent(s): 0217160

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -179,15 +179,17 @@ All quants made using imatrix option with dataset from [here](https://gist.githu
 | Filename | Quant type | File Size | Description |
 | -------- | ---------- | --------- | ----------- |
 | [Replete-Coder-Qwen-1.5b-Q8_0.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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_M.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q5_K_M.gguf) | Q5_K_M | 1125.04MB | High quality, *recommended*. |
-| [Replete-Coder-Qwen-1.5b-Q5_K_S.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF//main/Replete-Coder-Qwen-1.5b-Q5_K_S.gguf) | Q5_K_S |  | High quality, *recommended*. |
 | [Replete-Coder-Qwen-1.5b-Q4_K_M.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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-Q4_K_S.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF//main/Replete-Coder-Qwen-1.5b-Q4_K_S.gguf) | Q4_K_S |  | Slightly lower quality with more space savings, *recommended*. |
 | [Replete-Coder-Qwen-1.5b-IQ4_XS.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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_L.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/Replete-Coder-Qwen-1.5b-Q3_K_L.gguf) | Q3_K_L | 880.16MB | Lower quality but usable, good for low RAM availability. |
-| [Replete-Coder-Qwen-1.5b-IQ3_M.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF//main/Replete-Coder-Qwen-1.5b-IQ3_M.gguf) | IQ3_M |  | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
 ## Downloading using huggingface-cli

 | Filename | Quant type | File Size | Description |
 | -------- | ---------- | --------- | ----------- |
+| [Replete-Coder-Qwen-1.5b-Q8_0_L.gguf](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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](https://huggingface.co/bartowski/Replete-Coder-Qwen-1.5b-GGUF/blob/main/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