# Troubleshooting

Real failures we've hit deploying this repo to Hugging Face Inference Endpoints, and how to fix them. Read this first when the endpoint won't start.

---

## 1. `Unrecognized model ... Should have a model_type key in its config.json`

Endpoint logs end with a giant list of model types (`albert, align, ... m2m_100, ... zoedepth`) and `Application startup failed`.

**Cause.** The Hub repo doesn't actually contain model weights / `config.json`. Usually happens when `model_loader.py` was committed to git but never *executed* against the Hub (pushing the Python file ≠ running it).

**Check.** 

```bash
python3 -c "from huggingface_hub import HfApi; print([s.rfilename for s in HfApi().model_info('ericaRC/example').siblings])"
```

You should see `config.json`, `model.safetensors`, `tokenizer_config.json`, `tokenizer.json`, `handler.py`, `requirements.txt`, `README.md`. If it's only `.gitattributes` and scripts, the weights were never pushed.

**Fix.**

```bash
huggingface-cli login
python3 model_loader.py
```

---

## 2. `403 Forbidden` on `.../info/lfs/objects/batch`

`push_to_hub` dies with `HfHubHTTPError: 403 Forbidden: Authorization error.`

**Cause.** Your HF token lacks write access to the target repo. Most commonly: a fine-grained token scoped to your user only, trying to push to an org namespace. Reading works (which is why `whoami` succeeds) but LFS writes are rejected.

**Check.**

```bash
python3 -c "
from huggingface_hub import HfApi
perms = HfApi().whoami()['auth']['accessToken'].get('fineGrained', {})
for s in perms.get('scoped', []):
    print(s['entity']['type'], s['entity']['name'], '->', s['permissions'])
"
```

You need an entry matching the target repo's namespace (user or org) that includes `repo.write`.

**Fix.** At https://huggingface.co/settings/tokens either:
- Edit the existing token and add the org with `repo.write` + `repo.content.read` + `repo.access.read`, **or**
- Create a new classic "Write" token and `huggingface-cli login` with it.

---

## 3. `AttributeError: 'list' object has no attribute 'keys'` in `_set_model_specific_special_tokens`

Endpoint logs show a traceback through `tokenization_nllb_fast.py` → `tokenization_utils_base.py` and crash on:

```
self.SPECIAL_TOKENS_ATTRIBUTES + list(special_tokens.keys())
```

**Cause.** Transformers-version skew between save time and load time. `transformers` 5.x introduced an `extra_special_tokens` field (serialized as a list for NLLB's Flores-200 codes). The Inference Endpoints base image ships a `transformers` 4.x that expects `extra_special_tokens` to be a dict and calls `.keys()` on it.

**Check.**

```bash
python3 -c "
import json
from huggingface_hub import hf_hub_download
cfg = json.load(open(hf_hub_download('ericaRC/example', 'tokenizer_config.json')))
print('extra_special_tokens type:', type(cfg.get('extra_special_tokens')).__name__)
print('additional_special_tokens count:', len(cfg.get('additional_special_tokens') or []))
"
```

If `extra_special_tokens` is a non-empty `list` and `additional_special_tokens` is empty, you're hitting this.

**Fix (already applied to this repo).** `tokenizer_config.json` has been normalized:
- lang codes live in `additional_special_tokens` (list — old *and* new transformers accept this)
- `extra_special_tokens` is `{}` (empty dict — passes `.keys()` in old transformers, ignored in new)

And `requirements.txt` pins `transformers>=4.40.0,<5.0` to prevent the endpoint from auto-pulling a 5.x that re-introduces the mismatch.

**Prevention going forward.** When running `model_loader.py`, use the same `transformers` major version the endpoint runs:

```bash
pip install "transformers<5" "huggingface_hub" "torch"
python3 model_loader.py
```

Don't save tokenizers from `transformers` 5.x and load them in a 4.x container (or vice versa) unless you've confirmed the schema matches.

---

## 4. Endpoint boots but requests return garbage / wrong language

**Cause.** `src_lang` wasn't set on the tokenizer, or `forced_bos_token_id` wasn't passed at generation time. NLLB needs both.

**Check.** Look at the request body:

```json
{
  "inputs": "Hello, world!",
  "parameters": { "src_lang": "eng_Latn", "tgt_lang": "fra_Latn" }
}
```

If you're hitting the endpoint without a `parameters` block, `handler.py` falls back to `eng_Latn → spa_Latn`.

**Fix.** Always pass `src_lang` and `tgt_lang` using [Flores-200 codes](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200).

---

## 5. Container Type is set to "Text Generation Inference (TGI)"

TGI only supports decoder-only causal LMs. NLLB is seq2seq, so TGI will refuse to load it and `handler.py` will be ignored.

**Fix.** In the endpoint's Advanced configuration, set **Container Type → Default** (the HF inference toolkit). That container picks up `handler.py` automatically.

---

## Checklist before clicking Deploy

- [ ] `HfApi().model_info(REPO).siblings` lists `config.json`, `model.safetensors`, `tokenizer*.json`, `handler.py`, `requirements.txt`, `README.md`.
- [ ] `tokenizer_config.json` has `extra_special_tokens: {}` (or absent) and `additional_special_tokens` populated.
- [ ] `requirements.txt` pins `transformers<5`.
- [ ] Local smoke test passes:
  ```python
  from handler import EndpointHandler
  h = EndpointHandler("ericaRC/example")
  print(h({"inputs": "Hello, world!", "parameters": {"src_lang": "eng_Latn", "tgt_lang": "fra_Latn"}}))
  ```
- [ ] Endpoint Container Type = **Default**, not TGI.