👁 Baichuan
Baichuan
Baichuan 7B
Legacy
59.2KDownloads841LikesJun 2023Released8K tokensContextApache 2.0License40 BasicQuality
Baichuan 7B (7B parameters) requires approximately 13.9 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 16 GB of VRAM.
Get started
— copy & paste to run locallyCopy-paste commands to run Baichuan 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "baichuan-inc/Baichuan-7B" \
--hf-file "Baichuan-7B-Q4_K_M.gguf" \
-c 4096 -ngl 99Quick specs
Parameters7B
Architecturedense
Context8K tokens
Modalitytext
Min RAM2.7 GB
Rec. RAM4.3 GB (Q4_K_M)
LicenseApache 2.0
FamilyBaichuan
✓ Chat✓ Reasoning
About this model
- •在同尺寸模型中Baichuan-7B达到了目前SOTA的水平，参考下面MMLU指标
- •Baichuan-7B使用自有的中英文双语语料进行训练，在中文上进行优化，在C-Eval达到SOTA水平
- •不同于LLaMA完全禁止商业使用，Baichuan-7B使用更宽松的开源协议，允许用于商业目的
- •Among models of the same size, Baichuan-7B has achieved the current state-of-the-art (SOTA) level, as evidenced by the following MMLU metrics
- •Baichuan-7B is trained on proprietary bilingual Chinese-English corpora, optimized for Chinese, and achieves SOTA performance on C-Eval
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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 | 2.7 GB | Low | — |
Q3_K_S | 3 | 3.4 GB | Low | — |
NVFP4 | 4 | 3.9 GB | Medium | — |
Q4_K_M | 4 | 4.3 GB | Medium | — |
Q5_K_M | 5 | 5.0 GB | High | — |
Q6_K | 6 | 5.7 GB | High | — |
Q8_0 | 8 | 7.5 GB | Very High | — |
F16 | 16 | 14.3 GB | Maximum | — |
Quality benchmarks
Baichuan 7B benchmark scores
Reasoning
MMLU-Pro—
GPQA Diamond—
MATH-500—
ARC Challenge42.3%
Source: community · 2023-06-15
Hardware compatibility
Fit estimates across all hardware
Computing compatibility...
Memory breakdown
Reference: RTX 2060 6GB
Weights4.3 GB
KV Cache7.8 GB
Runtime1.2 GB
Headroom0.6 GB
Frequently asked questions
FAQ — Baichuan 7B
See also