👁 Tsinghua/Zhipu
Tsinghua/Zhipu
CogVLM2 19B
Current
6.0KDownloads220LikesMay 2024Released8K tokensContextApache 2.0License78 StrongQuality
CogVLM2 19B (19B parameters) requires approximately 15.5 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 18 GB of VRAM.
Get started
— copy & paste to run locallyCopy-paste commands to run CogVLM2 19B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "THUDM/cogvlm2-llama3-chat-19B" \
--hf-file "cogvlm2-llama3-chat-19B-Q4_K_M.gguf" \
-c 4096 -ngl 99Quick specs
Parameters19B
Architecturedense
Context8K tokens
Modalitytext+vision
Min RAM7.4 GB
Rec. RAM11.6 GB (Q4_K_M)
LicenseApache 2.0
FamilyCogVLM
✓ Vision✓ Chat
About this model
- •Significant improvements in many benchmarks such as TextVQA, DocVQA
- •Support 8K content length
- •Support image resolution up to **1344 * 1344**
- •Provide an open source model version that supports both Chinese and English
Your hardware
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Quantization options
VRAM estimates by quant level
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.4 GB | Low | — |
Q3_K_S | 3 | 9.3 GB | Low | — |
NVFP4 | 4 | 10.6 GB | Medium | — |
Q4_K_M | 4 | 11.6 GB | Medium | — |
Q5_K_M | 5 | 13.7 GB | High | — |
Q6_K | 6 | 15.6 GB | High | — |
Q8_0 | 8 | 20.3 GB | Very High | — |
F16 | 16 | 38.9 GB | Maximum | — |
Hardware compatibility
Fit estimates across all hardware
Computing compatibility...
Memory breakdown
Reference: RTX 2060 6GB
Weights11.6 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom0.6 GB
Frequently asked questions
FAQ — CogVLM2 19B
See also