talk to it — the Space, your own CPU, or the API; there is no widget for a 135M custom model

README.md

@@ -47,6 +47,57 @@ policies in the same repo.
 | framework | [mindXtrain](https://github.com/professor-codephreak/mindXtrain) 1.0.0 |
 | orchestration | [mastermind.pythai.net](https://mastermind.pythai.net) decides the campaign · [mindx.pythai.net](https://mindx.pythai.net) runs it |
 
+## 0. Talk to it — start here
+
+**There is no chat box on this page.** Hugging Face shows one only for models an inference provider
+serves, and no provider serves a 135M model trained on somebody's dreams. Three ways to actually
+speak to it, fastest first:
+
+**1 · In your browser, nothing to install**
+→ open **[mindXhfgradio](https://gregory-l-mindxhfgradio.hf.space/)** → the **Workbench** tab →
+set **backend = `here`** → type a question → press **probe**.
+The imprinted generation answers on the left, the untouched base on the right, and the coach scores
+the difference. Press **Sign in with Hugging Face** first: then the GPU minutes are your own
+(5/day free, 40 on PRO). Anonymous visitors share a small pool and are sometimes refused.
+
+**2 · On your own machine, no account needed**
+
+```bash
+pip install transformers torch
+python - <<'EOF'
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("PYTHAI/mindXtrain39")
+m   = AutoModelForCausalLM.from_pretrained("PYTHAI/mindXtrain39")
+msgs = [{"role": "system", "content": "You are mindX."}, {"role": "user", "content": "Who are you?"}]
+ids  = tok.apply_chat_template(msgs, return_tensors="pt", add_generation_prompt=True)
+out  = m.generate(ids, max_new_tokens=64, do_sample=False, repetition_penalty=1.3, no_repeat_ngram_size=3)
+print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
+EOF
+```
+
+It is 270 MB and answers on a laptop CPU in seconds. Or with Ollama:
+
+```bash
+huggingface-cli download PYTHAI/mindXtrain39 --local-dir mindXtrain39
+cd mindXtrain39 && ollama create mindXtrain39 --experimental -f Modelfile && ollama run mindXtrain39
+```
+
+**3 · From code or an agent**
+
+```python
+from gradio_client import Client                      # the Space, as an API
+c = Client("Gregory-L/mindXhfgradio")                 # add hf_token=… to spend your own quota
+print(c.predict("Who are you?", [], [], None, "here", 64, 0.0, False, "", api_name="/probe")[-1])
+```
+
+The same Space is an **MCP server** — add it at [settings/mcp](https://huggingface.co/settings/mcp)
+and `probe` becomes a tool in your client — and it publishes an
+[`agents.md`](https://huggingface.co/spaces/Gregory-L/mindXhfgradio/agents.md) for coding agents.
+
+**What to expect.** A 135M model with a recall imprint. It will echo mindX's corpus more than it will
+converse, and roughly one answer in six speaks as mindX. That is the measurement, not a disclaimer —
+see §9.
+
 ## 1. Use it
 
 ```python