base_model: distributedcognition/MiniCPM-V-4.7-35B-A3B-abliterated
base_model_relation: quantized
quantized_by: IsValorum
library_name: gguf
pipeline_tag: image-text-to-text
license: apache-2.0
language:
- en
- zh
tags: - gguf
- llama.cpp
- quantized
- apex
- apex-quant
- apex-i
- minicpm-v
- abliterated
- uncensored
- multimodal
- vision
- moe
- image-text-to-text
- imatrix
MiniCPM-V 4.7 35B A3B Abliterated - MiniPlus V2.1
APEX Quantization Series by IsValorum
[!IMPORTANT]
Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket.If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies.
[!NOTE]
Abliterated source: distributedcognition edited 74 tensors; no Heretic applied.Vision: use the companion
mmproj-Q8_0.gguf.DSpark: the main GGUF has 733 tensors and no MTP tensors; the DSpark Q4_K_M/Q6_K draft is a separate companion.
Family cross-links
| Tier | File | Base | Status |
|---|---|---|---|
| MiniPlus | MiniCPM-V-4.7-35B-A3B-Abliterated.APEX-I-MiniPlus-V2.1.gguf |
Abliterated BF16 | Published |
| NanoPlus | see repo | Abliterated BF16 | Published |
Collection: apex-i-miniplus-v21-standard-moe-6acfee77961c8c0cf5a083b5.
Architecture and precision
- Base model:
distributedcognition/MiniCPM-V-4.7-35B-A3B-abliterated(Apache-2.0), abliterated by distributedcognition fromopenbmb/MiniCPM-V-4.7-35B-A3B. - Architecture: 40 layers, 30 linear-attention/SSM + 10 full-attention; 256 routed experts + 1 shared, 8 active; hidden 2048; 262,144-token context; vision tower 27 layers.
- Main GGUF: 733 tensors. Vision is a separate Q8_0 projector; DSpark is a separate draft.
- iMatrix: public mradermacher matrix, 319 x 512; covers 400 of 402 quantized text tensors.
output.weightandtoken_embd.weightdo not use it. - Recurrent shielding:
ssm_alpha,ssm_a,ssm_dt.bias,ssm_conv1dand RMSNorms remainF32. - Layer 0 uses
Q4_0forattn_gate,attn_qkv,ssm_betaandssm_out.
Audited tensor map
| Tensor group | Layers | Count | Precision |
|---|---|---|---|
output.weight |
global | 1 | Q6_K |
output_norm.weight |
global | 1 | F32 |
token_embd.weight |
global | 1 | Q4_K |
blk.0.attn_gate.weight |
layer 0 | 1 | Q8_0 |
blk.*.attn_norm.weight, blk.*.post_attention_norm.weight |
all 40 | 80 | F32 |
blk.0.attn_qkv.weight |
layer 0 | 1 | Q3_K |
blk.*.ffn_down_exps.weight |
0-9 | 10 | Q3_K |
blk.*.ffn_down_shexp.weight |
all 40 | 40 | Q5_K |
blk.*.ffn_gate_exps.weight |
0-9 | 10 | Q3_K |
blk.*.ffn_gate_inp.weight |
all 40 | 40 | F32 |
blk.*.ffn_gate_inp_shexp.weight |
all 40 | 40 | F32 |
blk.*.ffn_gate_shexp.weight |
all 40 | 40 | Q5_K |
blk.*.ffn_up_exps.weight |
0-9 | 10 | Q3_K |
blk.*.ffn_up_shexp.weight |
all 40 | 40 | Q5_K |
blk.*.ssm_a |
SSM layers | 30 | F32 |
blk.*.ssm_alpha.weight |
SSM layers | 30 | F32 |
blk.0.ssm_beta.weight |
layer 0 | 1 | Q3_K |
blk.*.ssm_conv1d.weight |
SSM layers | 30 | F32 |
blk.*.ssm_dt.bias |
SSM layers | 30 | F32 |
blk.*.ssm_norm.weight |
SSM layers | 30 | F32 |
blk.0.ssm_out.weight |
layer 0 | 1 | Q3_K |
blk.*.attn_gate.weight |
SSM layers except 0 | 29 | Q8_0 |
blk.*.attn_qkv.weight |
SSM layers except 0 | 29 | Q3_K |
blk.*.ssm_beta.weight |
SSM layers except 0 | 29 | Q3_K |
blk.*.ssm_out.weight |
SSM layers except 0 | 29 | Q3_K |
blk.*.attn_k.weight |
full attention 3,7,...,39 | 10 | Q4_K |
blk.*.attn_k_norm.weight |
full attention 3,7,...,39 | 10 | F32 |
blk.*.attn_output.weight |
full attention 3,7,...,39 | 10 | Q6_K |
blk.*.attn_q.weight |
full attention 3,7,...,39 | 10 | Q4_K |
blk.*.attn_q_norm.weight |
full attention 3,7,...,39 | 10 | F32 |
blk.*.attn_v.weight |
full attention 3,7,...,39 | 10 | Q4_K |
blk.*.ffn_down_exps.weight |
10-29 | 20 | IQ3_XXS |
blk.*.ffn_gate_exps.weight |
10-29 | 20 | IQ3_XXS |
blk.*.ffn_up_exps.weight |
10-29 | 20 | IQ3_XXS |
blk.*.ffn_down_exps.weight |
30-39 | 10 | Q3_K |
blk.*.ffn_gate_exps.weight |
30-39 | 10 | Q3_K |
blk.*.ffn_up_exps.weight |
30-39 | 10 | Q3_K |
The machine-readable version is tensor-map.tsv.
Files
| File | Size | Notes |
|---|---|---|
MiniCPM-V-4.7-35B-A3B-Abliterated.APEX-I-MiniPlus-V2.1.gguf |
14,750,798,496 bytes | Main MiniPlus V2.1 GGUF |
| tensor-map.tsv | 33,146 bytes | Audited per-tensor types |
| chat_template.jinja | from source | MiniCPM-V 4.7 template |
| mmproj-Q8_0.gguf | 733,004,960 bytes | Vision projector |
| dspark Q4_K_M | 237,004,800 bytes | DSpark draft |
| dspark Q6_K | 330,315,072 bytes | DSpark draft |
Usage
llama-cli -m MiniCPM-V-4.7-35B-A3B-Abliterated.APEX-I-MiniPlus-V2.1.gguf -p "Hello" -ngl 99 -c 32768
llama-server -m MiniCPM-V-4.7-35B-A3B-Abliterated.APEX-I-MiniPlus-V2.1.gguf --mmproj mmproj-Q8_0.gguf --jinja -ngl 99 -c 32768
The main file has no MTP tensors. Use the DSpark companion separately if your runtime supports it.
Limitations
- Base benchmarks are self-reported by OpenBMB; abliteration evaluation is from distributedcognition.
- Vision and DSpark were not independently evaluated for this tier.
- This is a quantized deployment tier, not the BF16 reference.
License
Apache-2.0, inherited from the source model. See the source model card.