Instructions to use Melvin56/DeepScaleR-1.5B-Preview-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Melvin56/DeepScaleR-1.5B-Preview-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Melvin56/DeepScaleR-1.5B-Preview-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Melvin56/DeepScaleR-1.5B-Preview-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Melvin56/DeepScaleR-1.5B-Preview-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 Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Melvin56/DeepScaleR-1.5B-Preview-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 Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Melvin56/DeepScaleR-1.5B-Preview-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 Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Melvin56/DeepScaleR-1.5B-Preview-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Melvin56/DeepScaleR-1.5B-Preview-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": "Melvin56/DeepScaleR-1.5B-Preview-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M

SGLang

How to use Melvin56/DeepScaleR-1.5B-Preview-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 "Melvin56/DeepScaleR-1.5B-Preview-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": "Melvin56/DeepScaleR-1.5B-Preview-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 "Melvin56/DeepScaleR-1.5B-Preview-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": "Melvin56/DeepScaleR-1.5B-Preview-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Melvin56/DeepScaleR-1.5B-Preview-GGUF with Ollama:
```
ollama run hf.co/Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M
```

Unsloth Studio

How to use Melvin56/DeepScaleR-1.5B-Preview-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 Melvin56/DeepScaleR-1.5B-Preview-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 Melvin56/DeepScaleR-1.5B-Preview-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Melvin56/DeepScaleR-1.5B-Preview-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Melvin56/DeepScaleR-1.5B-Preview-GGUF with Docker Model Runner:
```
docker model run hf.co/Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M
```

Lemonade

How to use Melvin56/DeepScaleR-1.5B-Preview-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Melvin56/DeepScaleR-1.5B-Preview-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepScaleR-1.5B-Preview-GGUF-Q4_K_M

List all available models

lemonade list

Melvin56/DeepScaleR-1.5B-Preview-GGUF

Original Model : agentica-org/DeepScaleR-1.5B-Preview

All quants are made using the imatrix option.

	CPU (AVX2)	Metal	cuBLAS	rocBLAS	SYCL	CLBlast	Vulkan	Kompute
K-quants	✅	✅	✅	✅	✅	✅ 🐢5	✅ 🐢5	❌
I-quants	✅ 🐢4	✅ 🐢4	✅	✅	Partial¹	❌	❌	❌

✅: feature works.
🚫: feature does not work
❓: unknown, please contribute if you can test it youself
🐢: feature is slow
¹: IQ3_S and IQ1_S, see #5886
²: Only with -ngl 0
³: Inference is 50% slower
⁴: Slower than K-quants of comparable size
⁵: Slower than cuBLAS/rocBLAS on similar cards
⁶: Only q8_0 and iq4_nl

Model	Size (GB)
Q2_K	0.75
IQ3_XXS	0.76
IQ3_XS	0.83
IQ3_S	0.86
IQ3_M	0.87
Q3_K_M	0.92
IQ4_XS	1.01
Q4_K_M	1.12
Q5_K_M	1.28
Q6_K	1.46
Q8_0	1.89
F16	3.55

Downloads last month: 157

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Melvin56/DeepScaleR-1.5B-Preview-GGUF

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Finetuned

agentica-org/DeepScaleR-1.5B-Preview

Quantized

(38)

this model

Datasets used to train Melvin56/DeepScaleR-1.5B-Preview-GGUF

Collection including Melvin56/DeepScaleR-1.5B-Preview-GGUF

DeepScaleR-1.5B-Preview

Collection

1 item • Updated May 13, 2025