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glimmer-e4b-asd9-gguf
chenghuzi/glimmer-e4b-asd9-gguf
glimmer-e4b-asd9-gguf is a machine learning model from chenghuzi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for gguf. The card lists the license as apache-2.0.
Glimmer is a Gemma 4 E4B based GGUF model for observable behavior-label prediction. This private preview release is intended for research and screening-support workflows. It is not an ASD diagnostic system and must no…
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At a glance
- Library
- gguf
- License
- apache-2.0
- Access
- Public
- Created
- Jun 6, 2026
- Updated
- Jun 6, 2026
- SHA
- 77852a5c
Base models
Model card
From the Hugging Face model README
Glimmer E4B ASD-9 GGUF
Glimmer is a Gemma 4 E4B based GGUF model for observable behavior-label prediction. This private preview release is intended for research and screening-support workflows. It is not an ASD diagnostic system and must not be used as the sole basis for diagnosis, treatment decisions, or emergency decisions.
Files
model-Q4_K_M.gguf: Q4_K_M GGUF language model.mmproj-bf16.gguf: BF16 multimodal projection file. Use it together with the main GGUF file.metrics.json: Sanitized metric summary only. It does not include predictions, logs, media, cache files, or local paths.
Output Contract
The model is expected to emit a strict 9-bit code for B01 through B09.
^[01]{9}$
B10 is not directly predicted by the model. Applications should derive B10=true
only when all B01 through B09 bits are 0.
Labels
| Code | Label |
|---|---|
| B01 | Absence or Avoidance of Eye Contact |
| B02 | Aggressive Behavior |
| B03 | Hyper- or Hyporeactivity to Sensory Input |
| B04 | Non-Responsiveness to Verbal Interaction |
| B05 | Non-Typical Language |
| B06 | Object Lining-Up |
| B07 | Self-Hitting or Self-Injurious Behavior |
| B08 | Self-Spinning or Spinning Objects |
| B09 | Upper Limb Stereotypies |
| B10 | Background, application-derived |
Evaluation Summary
Evaluation used the unchanged test split with 182 samples.
The published metrics use the native-equivalent llama.cpp server baseline with pinned media marker
<__media__> and prompt cache disabled for iOS/native parity.
The run used Chinese prompts, audio enabled, up to 32 frames,
image width 512, max audio 30.0 seconds,
deterministic decoding, and grammar-constrained 9-bit output.
| Metric | Value |
|---|---|
| Parse rate | 1.0000 |
| Exact match | 0.3626 |
| Hamming accuracy | 0.8692 |
| Micro precision | 0.6059 |
| Micro recall | 0.4965 |
| Micro F1 | 0.5458 |
| Macro precision | 0.6461 |
| Macro recall | 0.5041 |
| Macro F1 | 0.5473 |
Per-Label Metrics
| Code | Label | Precision | Recall | F1 | Support |
|---|---|---|---|---|---|
| B01 | Absence or Avoidance of Eye Contact | 0.7895 | 0.3750 | 0.5085 | 40 |
| B02 | Aggressive Behavior | 0.9375 | 0.4688 | 0.6250 | 32 |
| B03 | Hyper- or Hyporeactivity to Sensory Input | 0.5714 | 0.3200 | 0.4103 | 25 |
| B04 | Non-Responsiveness to Verbal Interaction | 0.4375 | 0.2593 | 0.3256 | 27 |
| B05 | Non-Typical Language | 0.4286 | 0.3750 | 0.4000 | 24 |
| B06 | Object Lining-Up | 1.0000 | 0.7778 | 0.8750 | 9 |
| B07 | Self-Hitting or Self-Injurious Behavior | 0.3571 | 0.5263 | 0.4255 | 19 |
| B08 | Self-Spinning or Spinning Objects | 0.7222 | 0.5200 | 0.6047 | 25 |
| B09 | Upper Limb Stereotypies | 0.6000 | 0.5660 | 0.5825 | 53 |
| B10 | Background | 0.6170 | 0.8529 | 0.7160 | 34 |
llama.cpp Usage Notes
Use both GGUF files. The exact command depends on the local llama.cpp build and multimodal support. Keep decoding deterministic and constrain output to the 9-bit grammar when possible.
Example grammar:
root ::= bit bit bit bit bit bit bit bit bit
bit ::= "0" | "1"
License
The GGUF model files are distributed under Apache-2.0. See LICENSE and NOTICE.
- Library
- gguf
- License
- apache-2.0
- Created
- Jun 6, 2026
- Updated
- Jun 6, 2026