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DhruvalLabs/MiniCPM5-1B-GGUF
MiniCPM5-1B-GGUF is a text generation model from DhruvalLabs. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
Downloads · 30 days
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6% of all-time downloads
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.gguf7.2 GB · 100%
From the Hugging Face model README
Quantized GGUF versions of openbmb/MiniCPM5-1B
Works with llama.cpp · Ollama · LM Studio · Open WebUI · Jan
Quantized by Dhptl on June 18, 2026 using quant-kit
</div>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.
| Benchmark | Original (FP16) | Q4_K_M | Quality Retained |
|---|---|---|---|
| MMLU Pro | See original card | Run benchmarks | ~97-99% |
| HellaSwag | See original card | Run benchmarks | ~97-99% |
| ARC Challenge | See original card | Run benchmarks | ~97-99% |
| TruthfulQA | See original card | Run benchmarks | ~97-99% |
| GSM8K | See original card | Run benchmarks | ~97-99% |
| Filename | Size | RAM Required | Quant | Quality | Best For |
|---|---|---|---|---|---|
MiniCPM5-1B-Q2_K.gguf | 0.45 GB | ~2.0 GB | Q2_K | ⭐ | Extreme compression, significant quality loss. |
MiniCPM5-1B-Q3_K_L.gguf | 0.57 GB | ~2.1 GB | Q3_K_L | ⭐⭐⭐ | Slightly better than Q3_K_M, still a compromise. |
MiniCPM5-1B-Q3_K_M.gguf | 0.54 GB | ~2.0 GB | Q3_K_M | ⭐⭐⭐ | Very small file. Quality drop noticeable. |
MiniCPM5-1B-Q3_K_S.gguf | 0.51 GB | ~2.0 GB | Q3_K_S | ⭐⭐ | Very high compression, high quality loss. |
MiniCPM5-1B-Q4_K_M.gguf | 0.64 GB | ~2.1 GB | Q4_K_M ✅ Recommended | ⭐⭐⭐⭐ | Best balance of size and quality. Recommended for most users. |
MiniCPM5-1B-Q4_K_S.gguf | 0.62 GB | ~2.1 GB | Q4_K_S | ⭐⭐⭐½ | Good speed/size balance, slight quality loss. |
MiniCPM5-1B-Q5_K_M.gguf | 0.73 GB | ~2.2 GB | Q5_K_M | ⭐⭐⭐⭐½ | Better quality than Q4, slightly larger. Great if you have the RAM. |
MiniCPM5-1B-Q5_K_S.gguf | 0.72 GB | ~2.2 GB | Q5_K_S | ⭐⭐⭐⭐ | Large but accurate. |
MiniCPM5-1B-Q6_K.gguf | 0.83 GB | ~2.3 GB | Q6_K | ⭐⭐⭐⭐⭐ | Near-perfect quality, very large. |
MiniCPM5-1B-Q8_0.gguf | 1.07 GB | ~2.6 GB | Q8_0 | ⭐⭐⭐⭐⭐ | Closest to original quality. Use when RAM is not a concern. |
MiniCPM5-1B-Q4_K_M.gguf — best balance of size and qualityMiniCPM5-1B-Q8_0.gguf — near-original qualityMiniCPM5-1B-Q3_K_M.gguf — fits in 8GB with room to spareRun python benchmark.py --model MiniCPM5-1B to generate speed results.
Run kaggle_bench.ipynb on Kaggle to benchmark this model.
ollama run dhptl/minicpm5-1b
Search for Dhptl/MiniCPM5-1B in the model browser.
# Download the binary from https://github.com/ggerganov/llama.cpp/releases
./llama-cli \
-m MiniCPM5-1B-Q4_K_M.gguf \
-p "You are a helpful assistant." \
--conversation \
-n 512
from llama_cpp import Llama
llm = Llama(
model_path="./MiniCPM5-1B-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"])
GGUF is the standard file format for running large language models locally. Quantization reduces the number of bits per weight:
| Format | Bits/weight | Size vs FP16 | Quality |
|---|---|---|---|
| Q2_K | ~2.6 | 16% | ⭐ |
| Q3_K_M | ~3.3 | 21% | ⭐⭐⭐ |
| Q4_K_M | ~4.5 | 28% | ⭐⭐⭐⭐ ← sweet spot |
| Q5_K_M | ~5.6 | 35% | ⭐⭐⭐⭐½ |
| Q8_0 | ~8.5 | 53% | ⭐⭐⭐⭐⭐ |
Found an issue? Have a question? Open a Discussion in the Community tab above.
If these quantizations were useful, please consider: