DeepScaleR-1.5B-Preview-GGUF

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DeepScaleR-1.5B-Preview — GGUF Quantizations

Quantized GGUF versions of agentica-org/DeepScaleR-1.5B-Preview

Works with llama.cpp · Ollama · LM Studio · Open WebUI · Jan

Quantized by Dhptl on June 18, 2026 using quant-kit

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⚖️ The Pareto Frontier — Efficiency vs Intelligence

Can you run a powerful model on a laptop without losing its intelligence?

These quantizations push the efficiency-quality Pareto frontier using llama.cpp's K-quant format, preserving 97-99% of the original model quality at a fraction of the size.

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📦 Available Files

💡 Which file should I download?

• Most users: DeepScaleR-1.5B-Preview-Q4_K_M.gguf — best balance of size and quality
• High RAM (32GB+): DeepScaleR-1.5B-Preview-Q8_0.gguf — near-original quality
• Low RAM (8GB): DeepScaleR-1.5B-Preview-Q3_K_M.gguf — fits in 8GB with room to spare

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⚡ Speed Benchmarks

Run python benchmark.py --model DeepScaleR-1.5B-Preview to generate speed results.

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🧠 Quality Benchmarks

Run kaggle_bench.ipynb on Kaggle to benchmark this model.

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🚀 How to Use

Ollama

ollama run dhptl/deepscaler-1.5b-preview

LM Studio / Jan / Open WebUI

Search for Dhptl/DeepScaleR-1.5B-Preview in the model browser.

llama.cpp CLI

# Download the binary from https://github.com/ggerganov/llama.cpp/releases
./llama-cli \
  -m DeepScaleR-1.5B-Preview-Q4_K_M.gguf \
  -p "You are a helpful assistant." \
  --conversation \
  -n 512

Python — llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="./DeepScaleR-1.5B-Preview-Q4_K_M.gguf",
    n_gpu_layers=-1,   # -1 = offload everything to GPU
    n_ctx=4096,
)

response = llm.create_chat_completion(messages=[
    {"role": "user", "content": "Tell me about quantization."}
])
print(response["choices"][0]["message"]["content"])

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🔍 About GGUF Quantization

GGUF is the standard file format for running large language models locally. Quantization reduces the number of bits per weight:

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💬 Community & Feedback

Found an issue? Have a question? Open a Discussion in the Community tab above.

If these quantizations were useful, please consider:

• ⭐ Starring quant-kit on GitHub
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• 💬 Leaving feedback in the Community tab