Model page

unaidedelf87777/nexus-mistral-v1-ep34 warn

Weights only ship in a format that can run code when loaded; loading it runs custom code from the repo; no license declared — no usage rights by default. Plus 1 more issue.

downloads 17likes 0license none declaredarch mistralupdated 2023-11-03

chat template: not found · view on Hugging Face ↗

Scan coverageStatic battery2026-08-25Weights batteryqueuedBehavioral batterynot rundetails
BatteryLooks atStatus
Static batteryMetadata & packagingcomplete 2026-08-25
Weights batteryWeights forensics — no GPU, no downloadqueued
Behavioral batteryLive-inference differentialsnot 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 (bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt, bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt, bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt, …). 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.

  1. Do not load the pickle files in-process — pickle deserialization executes arbitrary code from the file.
  2. 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.
  3. Pin the exact revision hash you converted from, and load only your converted safetensors artifact from then on.

medium Repo ships executable Python (trust_remote_code)

The repository contains custom code files that run in-process when loaded with trust_remote_code=True. Pin the revision hash and review the code before loading.

How to fix

Review and pin the custom code; never float on `main` with trust_remote_code=True.

  1. Read every `.py` file in the repo before first load — this code runs in your process.
  2. Pin the revision: `from_pretrained(model_id, revision="<commit sha>", trust_remote_code=True)` so a later push can't swap the code under you.
  3. Prefer a version of the architecture already in `transformers` if one exists, which removes the remote-code requirement entirely.

medium No license declared

The model card declares no license. You have no usage rights by default — treat as all-rights-reserved until the owner clarifies.

How to fix

Get a license from the owner or pick a licensed alternative — this is a legal gap, not a technical one.

  1. With no declared license you have no usage rights by default; treat the weights as all-rights-reserved.
  2. Open an issue or discussion on the repo asking the owner to declare a license, or use the licensed upstream/parent model instead.

medium EOS ids disjoint between config.json and generation_config.json

config.json declares eos_token_id [32000] while generation_config.json declares [2] with no overlap. Runtimes read one or the other, so at least one of them stops generation on the wrong token (or never). Align both files on the token the chat template actually ends turns with.

How to fixingot patch

Align the stop-token declarations — a pure metadata fix to `generation_config.json` (and `config.json`).

  1. 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.
  2. Keep `config.json`'s `eos_token_id` consistent with (or a subset of) `generation_config.json`'s — runtimes differ in which file they read.
  3. For the pad-equals-EOS hazard: repoint `pad_token` at a dedicated padding token before fine-tuning; serving is unaffected.
  4. 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`).

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 unaidedelf87777/nexus-mistral-v1-ep34

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.

architecturemistral · 32 layers · 4096-dim
vocabulary32,002 tokens
licensenone declared
serializationno safetensors pickle custom code
chat templatenone
Full measured fingerprint
architecturesMistralForCausalLM
librarytransformers
pipelinetext-generation
repo files32 — pickle: bf16_zero_pp_rank_0_mp_rank_00_optim_states.pt, bf16_zero_pp_rank_1_mp_rank_00_optim_states.pt, bf16_zero_pp_rank_2_mp_rank_00_optim_states.pt, bf16_zero_pp_rank_3_mp_rank_00_optim_states.pt, bf16_zero_pp_rank_4_mp_rank_00_optim_states.pt, bf16_zero_pp_rank_5_mp_rank_00_optim_states.pt, bf16_zero_pp_rank_6_mp_rank_00_optim_states.pt, bf16_zero_pp_rank_7_mp_rank_00_optim_states.pt, mp_rank_00_model_states.pt, pytorch_model-00001-of-00002.bin
revision73e8cdd01b07
HF snapshot17 downloads · 0 likes · updated 2023-11-03 · captured 2026-08-25
Battery runs (1)the run trace behind the findings — what each job measured
batterystatusqueueddurationattempts
weightsqueued2026-08-25 22:210

Verdict badge

Ship the verdict in your README — it always shows the latest published analysis:

Ingot verdict: warn

[![Ingot scan](https://ingot.tools/api/v1/models/unaidedelf87777/nexus-mistral-v1-ep34/badge.svg)](https://ingot.tools/models/unaidedelf87777/nexus-mistral-v1-ep34)
Gate it in CI