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README.md

@@ -21,6 +21,46 @@ on a single consumer GPU (RTX 5060 Ti, 16 GB).
 It is fluent in Maltese but hallucinates and does not always reply with anything related to the prompt.
 It's able to translate english sentences into a related sentence in Maltese. It's quite bad at it but the Maltese translation is related somehow.
 
+## How to run
+
+### Ollama
+
+```sh
+ollama run hf.co/Cabbache/Fredu-1.7B-Instruct
+```
+
+The prompt template and sampling parameters are stored in the repo, so nothing
+else is needed. Pick a build with a tag:
+
+| tag | size | notes |
+|---|---|---|
+| *(none)* / `:Q4_K_M` | 1.0 GB | 4-bit, the default. Runs on a laptop, loses the most accuracy |
+| `:Q8_0` | 1.8 GB | 8-bit, close to full quality |
+| `:F16` | 3.3 GB | no quantization |
+
+The weights were trained in bfloat16, so even `F16` is a small conversion away
+from the `safetensors` originals rather than a bit-exact copy.
+
+### transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("Cabbache/Fredu-1.7B-Instruct")
+model = AutoModelForCausalLM.from_pretrained("Cabbache/Fredu-1.7B-Instruct",
+                                             dtype="bfloat16", device_map="auto")
+
+q = "X'inhu l-Kunsill Lokali f'Malta?"
+ids = tok(f"Mistoqsija: {q}\nTweġiba:", return_tensors="pt").to(model.device)
+out = model.generate(**ids, max_new_tokens=200, temperature=0.3, do_sample=True)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+
+There is no chat template — use the `Mistoqsija:/Tweġiba:` framing above, which
+is what the model was trained on. A low temperature helps: what factual
+knowledge it has is held with very little margin and does not survive sampling
+at 0.7.
+
 ## What was done
 
 | stage | data | result |