RedHatAI/TinyLlama-1.1B-Chat-v1.0-pruned2.4 warn
Weights only ship in a format that can run code when loaded; glitch tokens that can silently corrupt ordinary input. Plus 1 minor note.
Could not load this model from the Hugging Face API (private, gated, or nonexistent). Findings below are from our archive.
Scan coverageStatic battery2026-08-25Weights battery2026-08-25Behavioral batterynot rundetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-25 |
| Weights battery | Weights forensics — no GPU, no download | complete 2026-08-2532,000-token embedding scanned · 139 undertrained · lineage consistent · pickle audit clean |
| Behavioral battery | Live-inference differentials | not run |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-25 · published from a community scan.
medium Pickle-serialized weights, no safetensors
Weights ship only as pickle-based files (pytorch_model.bin). Loading pickle executes arbitrary code from the file — prefer a safetensors release or load in a sandbox.
How to fix
Convert the weights to safetensors before loading them anywhere that matters.
- Do not load the pickle files in-process — pickle deserialization executes arbitrary code from the file.
- Convert locally in a sandbox: `pip install safetensors` and use `safetensors.torch.save_file` on a state dict loaded with `torch.load(..., weights_only=True)` (refuses most code-execution payloads), or use Hugging Face's `convert.py` space/script.
- Pin the exact revision hash you converted from, and load only your converted safetensors artifact from then on.
low Padding token is the EOS token
The pad token and the (only) EOS token are the same. Fine-tuning frameworks mask pad positions out of the loss, so training on this checkpoint teaches the model to never emit EOS — the Phi-4 / Qwen 2.5 / DeepSeek R1 infinite-generation bug. Safe to serve, hazardous to fine-tune; repoint pad_token at a dedicated token first.
How to fixingot patch
Align the stop-token declarations — a pure metadata fix to `generation_config.json` (and `config.json`).
- Identify the token the chat template actually ends assistant turns with (e.g. `<|eot_id|>`, `<end_of_turn>`, `<|im_end|>`) and make sure its id is in `generation_config.json`'s `eos_token_id` list.
- Keep `config.json`'s `eos_token_id` consistent with (or a subset of) `generation_config.json`'s — runtimes differ in which file they read.
- For the pad-equals-EOS hazard: repoint `pad_token` at a dedicated padding token before fine-tuning; serving is unaffected.
- Until the repo is fixed, pass explicit stop tokens to your serving stack (e.g. vLLM `stop_token_ids`, llama.cpp `--override-kv tokenizer.ggml.eos_token_id`).
medium Undertrained (glitch) token surface in vocabulary
Embedding-norm scan flagged 139 undertrained tokens (norm < 0.3× the vocabulary median of 0.681), including 19 plain-ASCII strings that can appear in ordinary input as identifiers — e.g. "<0xFA>", "<0xFB>", "<0xFC>", "<0xFD>", "<0xFE>", "<0xFF>", "Mediabestanden", "autorytatywna". In models where this class was tested behaviorally, such tokens silently rewrote user input into confident, schema-valid, wrong output. These are candidates from the weights alone; behavioral confirmation requires the behavioral battery.
How to fixruntime guardweight-level
Keep the affected token strings out of the model's input — the scan-derived runtime guard carries this model's exact blocklist.
- Fetch this model's guard artifact (`/api/v1/guard/<owner>/<model>`): the confirmed corrupting tokens and the low-norm candidate list, derived from the published scan.
- Screen inbound text with it (the `@ingotai/guard` package is a reference implementation) and route flagged records to a different model or human review — verbatim-copy tasks on flagged strings are the failure mode.
- The underlying cause is undertrained embeddings in the weights; a true fix is weight-level (continued pretraining on the affected tokens) — that is not a patch, it's a training job.
info Pickle static analysis clean
Opcode-level parse of pytorch_model.bin (no code executed) found only standard serialization globals (3 distinct, all torch/collections/numpy). Pickle remains an executable format — this verifies the current bytes, not future uploads; prefer a safetensors release.
info Weights consistent with claimed parent TinyLlama/TinyLlama-1.1B-Chat-v1.0
Mean cosine similarity of 64 sampled token-embedding rows against TinyLlama/TinyLlama-1.1B-Chat-v1.0 is 0.984 — the weights plausibly descend from the declared base (relation: unspecified).
How to fix
Fix or verify the `base_model` declaration so lineage checks can run.
- If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
- If you don't: identify the true parent (config architecture + weight shapes narrow it fast) and re-scan with that lineage in mind.
Fix it
Some findings are metadata-level and patchable — apply the fixes to your local copy (your weights never leave your machine):
npx @ingotai/scan patch RedHatAI/TinyLlama-1.1B-Chat-v1.0-pruned2.4
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Fingerprint
The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-25.
| architecture | llama · 22 layers · 2048-dim |
| vocabulary | 32,000 tokens |
| license | apache-2.0 |
| serialization | no safetensors pickle |
| chat template | present · sha256:66291cf0045c2425 |
| claimed lineage | TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
| lineage verified | consistent vs TinyLlama/TinyLlama-1.1B-Chat-v1.0 — embedding-row cosine 0.984 |
| glitch-token surface | 139 undertrained candidates, 19 plain-ASCII |
Full measured fingerprint
| architectures | LlamaForCausalLM |
| library | transformers |
| pipeline | text-generation |
| repo files | 9 — pickle: pytorch_model.bin |
| revision | 25448498ef25 |
| HF snapshot | 5.3k downloads · 2 likes · updated 2026-05-05 · captured 2026-08-25 |
| pickle audit | pytorch_model.bin — 3 standard global(s) |
| embedding tensor | model.embed_tokens.weight · F16 · 32,000×2048 |
| embedding norms | median 0.6805 · mean 0.6678 |
| lineage check | consistent — cosine 0.9844 over 64 sampled rows vs TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
| glitch-token samples | "<0xFA>", "<0xFB>", "<0xFC>", "<0xFD>", "<0xFE>", "<0xFF>", "Mediabestanden", "autorytatywna", "Webachiv", "IABot", "demsel", "regnig" |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-25 21:46 | 39s | 1 |
weights run 2026-08-25 — measurements
| probes run | glitch-norm-scan, zero-template-token-scan, pickle-static-analysis, lineage-norm-correlation |
| embedding tensor | model.embed_tokens.weight · F16 · 32,000×2048 |
| glitch surface | 139 undertrained, 19 plain-ASCII |
| lineage check | consistent — cosine 0.9844 over 64 rows vs TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/RedHatAI/TinyLlama-1.1B-Chat-v1.0-pruned2.4)