Yes @Narsil, I’m using GPU.
Note that this is my OTHER model. You helped me load the shaxpir/prosecraft_linear_43195 with GPU pinning last week, and it’s been working great!
This other model (shaxpir/prosecraft_resumed_ft2) was built using a similar (but not identical) training process, and when I try running it (after pinning to a GPU device), I always get this error:
{"error":"Could not load model shaxpir/prosecraft_resumed_ft2 with any of the following classes: (<class 'transformers.models.gptj.modeling_gptj.GPTJForCausalLM'>,)."}

My API call looks like this…
curl -i -X POST https://api-inference.huggingface.co/models/shaxpir/prosecraft_resumed_ft2 \
     -H "Authorization: Bearer <<REDACTED>>" \
     -H "Content-Type: application/json" \
     -d \
     '{
          "inputs":"The witch stared through the tiny peephole in her front door and",
          "options":{
            "use_gpu": true,
            "use_cache": false
          },
          "parameters": {
            "return_full_text": false,
            "num_return_sequences": 1,
            "temperature": 1.0,
            "top_p" : 0.9,
            "max_new_tokens": 250
          }
     }'

Even though I’m using the use_gpu argument, and I’ve enabled GPU pinning on the model, the response headers look like this:
HTTP/1.1 400 Bad Request
date: Thu, 09 Dec 2021 21:03:22 GMT,Thu, 09 Dec 2021 21:04:29 GMT
server: istio-envoy
content-length: 167
content-type: application/json
x-envoy-upstream-service-time: 22266
x-compute-type: +optimized

On my other model, which ran successfully, I saw the x-compute-type response header had the value  gpu+optimized, whereas this one is just +optimized. Maybe that’s a clue that’ll help track down the problem?