This repository hosts the GGUF (llama.cpp) quantized version of MiniCPM-V-4.7-1B-DSpark. For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.7-1B-DSpark.

MiniCPM-V-4.7-1B-DSpark-GGUF

MiniCPM-V-4.7-1B-DSpark is a DSpark draft checkpoint trained for exact pairing with MiniCPM-V-4.7-1B and its tokenizer.

Model Specification

Training Recipe

Training responses were generated by MiniCPM-V-4.7-1B from a mixture of Vision & Text prompts.

Evaluation

Acceptance length is defined as the total number of completion tokens divided by the total number of speculative verification steps. Evaluation uses natural EOS termination and max_new_tokens=8192.

Usage with llama.cpp

Download the target model, its vision projector (mmproj), and this DSpark draft:

huggingface-cli download openbmb/MiniCPM-V-4.7-1B-gguf MiniCPM-V-4_7-1B-Q4_K_M.gguf mmproj-model-f16.gguf --local-dir ./1B-gguf
huggingface-cli download openbmb/MiniCPM-V-4.7-1B-DSpark-gguf dspark-MiniCPM-V-4_7-1B-Q8_0.gguf --local-dir ./1B-gguf

Run with speculative decoding (-md = draft, --spec-type draft-dspark). llama-cli handles both the vision projector and the draft:

llama-cli \
  -m ./1B-gguf/MiniCPM-V-4_7-1B-Q4_K_M.gguf \
  --mmproj ./1B-gguf/mmproj-model-f16.gguf \
  -md ./1B-gguf/dspark-MiniCPM-V-4_7-1B-Q8_0.gguf \
  --spec-type draft-dspark \
  --image image.jpg -p "Describe this image." \
  -ngl 99 -ngld 99 -fa on -c 8192

For a multimodal server:

llama-server \
  -m ./1B-gguf/MiniCPM-V-4_7-1B-Q4_K_M.gguf \
  --mmproj ./1B-gguf/mmproj-model-f16.gguf \
  -md ./1B-gguf/dspark-MiniCPM-V-4_7-1B-Q8_0.gguf \
  --spec-type draft-dspark --spec-draft-n-max 15 \
  -ngl 99 -ngld 99 -fa on -c 8192 --jinja --port 8080

Requires a llama.cpp build with the dflash architecture and the DSpark Markov head. The draft must be paired with the matching MiniCPM-V 4.7 1B target. Draft quantization only affects acceptance length (never the output), so pick the smallest quant that fits — Q8_0 / Q6_K are good defaults.

Limitations and Disclaimer

This model has no autonomous intent or legal personhood; its outputs are text generated from statistical patterns and may be inaccurate, biased, or offensive, and may be manipulated by carefully crafted prompts ("jailbreaks") into producing unintended content. Its responses on sensitive topics such as politics, health, finance, and law are not reviewed by experts and should not be treated as professional advice.

This model is provided "AS IS", without warranty of any kind, express or implied, and the developers are not liable for any damages arising from its use. Users must employ the model only for lawful, compliant, and ethical purposes, configure their own safeguards, and label AI-generated content where required; deliberate jailbreaking, injection attacks, or inducing harmful output is prohibited, and any such testing is at the user's own risk.