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john-before.gguf
Format: GGUF  ·  Size: 240.8 MB  ·  Scanned: 2026-08-23  ·  926 ms
SHA256: 1809955eb25d7667f2c2cd8adc269677866ff244…

Security grade

A
Score: 94/100
2 LOW 1 INFO
Artifact Security
A / 94
Repository Trust
N/A
local upload
Deployment Confidence
Medium
Artifact Security grades the file. Repository Trust is unavailable for local uploads — no external provenance to verify. Deployment Confidence reflects artifact grade only.
▶ Score breakdown
Base score 100
LOW No license field in GGUF metadata (general.license) -3
LOW No embedded provenance metadata -3
Penalty subtotal 94/100
Final A / 94
GGUF v3 model | architecture: gpt2 | size: 124M | quantization: F16 | 148 tensors | 2 warnings / 1 informational

Runtime compatibility

Runtime Status Quant Architecture Context
llama.cpp ✓ ✓ ✓ 1k
Ollama ✓ ✓ ✓ 1k
LM Studio ✓ ✓ ✓ 1k
vLLM ✗ N/A GGUF not supported — requires safetensors format
HuggingFace Transformers ✗ N/A GGUF not directly supported — use safetensors or PyTorch checkpoint

Model info

Architecture
Neural network family — determines which runtime can load this model
gpt2
NameCheckpoint Before Edit
Size label
Human-readable parameter count from model metadata
124M
Quantization
Weight storage format derived from actual tensor dtypes; lower bits = smaller file and faster inference at the cost of accuracy
 F16
Parameters ~
Estimated total number of weight values; determines VRAM needed at inference
124,439,808
Context length
Maximum tokens the model can process in a single prompt+response; affects KV-cache memory
1,024 tokens
Embedding dim
Hidden state dimension (d_model); larger = more expressive but more compute per token
768
FFN dim
Feed-forward network intermediate size; typically 2.7–4× embedding_length
3,072
Layers
Number of transformer blocks (depth); more layers = more reasoning capacity
12
Attention heads (Q)
Number of query heads; GQA/MQA models use fewer KV heads than Q heads
12
Vocab size
Number of unique tokens the tokenizer knows; affects embedding table size
50,257
BOS / EOS tokens
Beginning-of-sequence and end-of-sequence token IDs used by the tokenizer
50256 / 50256
Tokenizer
Tokenizer algorithm family (e.g. gpt2 = BPE, llama = SentencePiece)
gpt2
Chat template
Jinja2 prompt template embedded in model; controls how messages are formatted for inference
✗ No
Tensors
Total number of weight tensors stored in the file
148
File size
Size on disk in megabytes
240.77 MB

Security findings (3)

LOW Supply Chain
No license field in GGUF metadata (general.license)
Absence of license information makes it impossible to determine usage rights
→ Add general.license to model metadata before distributing; use an SPDX identifier
LOW Supply Chain
No embedded provenance metadata
None of general.author, general.source.url, general.base_model.0, or general.source.huggingface.repository are embedded in the GGUF file. Without any origin signal the model cannot be traced to a trusted source or compared against a reference checkpoint for tampering detection. For files uploaded directly, provenance is especially critical because the scanner cannot infer repository origin from the file alone.
→ Add at least one of: general.author, general.source.huggingface.repository, or general.source.url
INFO Embedding Size
Large embedding tensor 'token_embd.weight': 77,194,752 bytes
vocab=50,257 x emb_dim=768 x 2.0 B/elem -> expected 77,194,752 B — size is consistent with architecture
→ No action needed; large embeddings are expected for large-vocabulary models

Integrity & Structure

SHA-2561809955eb25d7667f2c2cd8adc269677866ff244467cd4c83da6d04074e67ec1
SHA-5120957398901f34b405eaacd0a9a640dc8171a264cd762c9869e180e3a9935caf46ef2c42c07727888…
File size (bytes)252,470,336
Header + KV + tensor directory1,775,168 bytes (1.69 MB)
Tensor data region250,695,168 bytes (239.08 MB)
Trailing bytes0
Unaccounted bytes0 ✓
KV metadata keys20
Tensors parsed148
Offsets monotone✓ OK
Max tensor offset173,500,416

DType Mix

DTypeCountTotal bytesTotal MBAvg bits/elem
F16 49 247,064,064 235.62 16.0
F32 99 3,631,104 3.46 32.0

Tensor Preview (first 50 of 148)

#NameDimsDTypeOffsetBytesbits/elem
0 blk.0.attn_qkv.bias [2304] F32 0 9,216 32.0
1 blk.0.attn_qkv.weight [768, 2304] F16 9,216 3,538,944 16.0
2 blk.0.attn_output.bias [768] F32 3,548,160 3,072 32.0
3 blk.0.attn_output.weight [768, 768] F16 3,551,232 1,179,648 16.0
4 blk.0.attn_norm.bias [768] F32 4,730,880 3,072 32.0
5 blk.0.attn_norm.weight [768] F32 4,733,952 3,072 32.0
6 blk.0.ffn_norm.bias [768] F32 4,737,024 3,072 32.0
7 blk.0.ffn_norm.weight [768] F32 4,740,096 3,072 32.0
8 blk.0.ffn_up.bias [3072] F32 4,743,168 12,288 32.0
9 blk.0.ffn_up.weight [768, 3072] F16 4,755,456 4,718,592 16.0
10 blk.0.ffn_down.bias [768] F32 9,474,048 3,072 32.0
11 blk.0.ffn_down.weight [3072, 768] F16 9,477,120 4,718,592 16.0
12 blk.1.attn_qkv.bias [2304] F32 14,195,712 9,216 32.0
13 blk.1.attn_qkv.weight [768, 2304] F16 14,204,928 3,538,944 16.0
14 blk.1.attn_output.bias [768] F32 17,743,872 3,072 32.0
15 blk.1.attn_output.weight [768, 768] F16 17,746,944 1,179,648 16.0
16 blk.1.attn_norm.bias [768] F32 18,926,592 3,072 32.0
17 blk.1.attn_norm.weight [768] F32 18,929,664 3,072 32.0
18 blk.1.ffn_norm.bias [768] F32 18,932,736 3,072 32.0
19 blk.1.ffn_norm.weight [768] F32 18,935,808 3,072 32.0
20 blk.1.ffn_up.bias [3072] F32 18,938,880 12,288 32.0
21 blk.1.ffn_up.weight [768, 3072] F16 18,951,168 4,718,592 16.0
22 blk.1.ffn_down.bias [768] F32 23,669,760 3,072 32.0
23 blk.1.ffn_down.weight [3072, 768] F16 23,672,832 4,718,592 16.0
24 blk.10.attn_qkv.bias [2304] F32 28,391,424 9,216 32.0
25 blk.10.attn_qkv.weight [768, 2304] F16 28,400,640 3,538,944 16.0
26 blk.10.attn_output.bias [768] F32 31,939,584 3,072 32.0
27 blk.10.attn_output.weight [768, 768] F16 31,942,656 1,179,648 16.0
28 blk.10.attn_norm.bias [768] F32 33,122,304 3,072 32.0
29 blk.10.attn_norm.weight [768] F32 33,125,376 3,072 32.0
30 blk.10.ffn_norm.bias [768] F32 33,128,448 3,072 32.0
31 blk.10.ffn_norm.weight [768] F32 33,131,520 3,072 32.0
32 blk.10.ffn_up.bias [3072] F32 33,134,592 12,288 32.0
33 blk.10.ffn_up.weight [768, 3072] F16 33,146,880 4,718,592 16.0
34 blk.10.ffn_down.bias [768] F32 37,865,472 3,072 32.0
35 blk.10.ffn_down.weight [3072, 768] F16 37,868,544 4,718,592 16.0
36 blk.11.attn_qkv.bias [2304] F32 42,587,136 9,216 32.0
37 blk.11.attn_qkv.weight [768, 2304] F16 42,596,352 3,538,944 16.0
38 blk.11.attn_output.bias [768] F32 46,135,296 3,072 32.0
39 blk.11.attn_output.weight [768, 768] F16 46,138,368 1,179,648 16.0
40 blk.11.attn_norm.bias [768] F32 47,318,016 3,072 32.0
41 blk.11.attn_norm.weight [768] F32 47,321,088 3,072 32.0
42 blk.11.ffn_norm.bias [768] F32 47,324,160 3,072 32.0
43 blk.11.ffn_norm.weight [768] F32 47,327,232 3,072 32.0
44 blk.11.ffn_up.bias [3072] F32 47,330,304 12,288 32.0
45 blk.11.ffn_up.weight [768, 3072] F16 47,342,592 4,718,592 16.0
46 blk.11.ffn_down.bias [768] F32 52,061,184 3,072 32.0
47 blk.11.ffn_down.weight [3072, 768] F16 52,064,256 4,718,592 16.0
48 blk.2.attn_qkv.bias [2304] F32 56,782,848 9,216 32.0
49 blk.2.attn_qkv.weight [768, 2304] F16 56,792,064 3,538,944 16.0
… 98 more tensors omitted from preview

Scan coverage

Checks performed
  • ✓Magic bytes & GGUF version validation
  • ✓Header parse (tensor count, KV count)
  • ✓KV metadata parse — all key/value pairs
  • ✓Tensor directory parse (names, dims, dtype, offsets)
  • ✓Tensor offset monotonicity
  • ✓Tensor overlap detection
  • ✓Tensor out-of-bounds detection
  • ✓Data section alignment verification
  • ✓Trailing bytes after last tensor
  • ✓Unaccounted bytes (header vs file size)
  • ✓Dtype histogram & quantization mix analysis
  • ✓Embedding tensor size cross-validation
  • ✓Chat template static scan (Jinja2/SSTI patterns)
  • ✓Runtime compatibility heuristics
  • —External model card / repository context: not available for local upload
  • —Upstream repository: not checked — not available for direct file upload
Not covered
  • ✗Runtime execution or dynamic analysis
  • ✗Behavioral backdoor detection
  • ✗Weight-level semantic backdoor detection
  • ✗Full upstream repository verification
  • ✗License validation beyond embedded metadata
  • ✗Malware scanning of surrounding repo files
  • ✗Adversarial robustness or alignment audit
  • ✗Training data provenance
ⓘ Static scanning can detect structural anomalies and known malicious patterns. It cannot guarantee absence of risk. Treat results as a security signal, not a formal audit.
Raw scan data (.llmscan JSON) 
{
  "compatibility": [
    {
      "arch": true,
      "container": true,
      "context": "1k",
      "notes": "GGUF v3 container: supported | quant F16: supported",
      "quant": true,
      "runtime": "llama.cpp",
      "supported": true
    },
    {
      "arch": true,
      "container": true,
      "context": "1k",
      "notes": "GGUF via llama.cpp backend: container and quantization supported",
      "quant": true,
      "runtime": "Ollama",
      "supported": true
    },
    {
      "arch": true,
      "container": true,
      "context": "1k",
      "notes": "GGUF container and quantization supported; GPU/CPU inference",
      "quant": true,
      "runtime": "LM Studio",
      "supported": true
    },
    {
      "arch": null,
      "container": false,
      "context": null,
      "not_supported_msg": "GGUF not supported \u2014 requires safetensors format",
      "notes": "GGUF not supported; requires safetensors format",
      "quant": false,
      "runtime": "vLLM",
      "supported": false
    },
    {
      "arch": null,
      "container": false,
      "context": null,
      "not_supported_msg": "GGUF not directly supported \u2014 use safetensors or PyTorch checkpoint",
      "notes": "GGUF not supported; requires safetensors or PyTorch checkpoint",
      "quant": false,
      "runtime": "HuggingFace Transformers",
      "supported": false
    }
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  "format": "LLM-SCAN",
  "format_version": "1.0",
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