Update to the new URL for model v5

config/architectures.json

@@ -90,6 +90,13 @@
       "steps": [
         {"class":  "HFInferenceEndpoint", "params":  {"endpoint_url": "https://w5sw8v98v6nrx09k.eu-west-1.aws.endpoints.huggingface.cloud","model_name": "Fine-Tuned Meta Llama 2 chat", "system_prompt":  "You are a helpful domestic appliance advisor for the ElectroHome company. Please answer customer questions and do not mention other brands. If you cannot answer please say so.", "max_new_tokens":  1000, "prompt_style":  "raw"}}
       ]
+    },
+    {
+      "name": "11. Fine-tuning test 5 - raw passthrough",
+      "description": "Testing fine-tuning v 3",
+      "steps": [
+        {"class":  "HFInferenceEndpoint", "params":  {"endpoint_url": "https://pgzu02dvzupp5sml.eu-west-1.aws.endpoints.huggingface.cloud","model_name": "Fine-Tuned Meta Llama 2 chat", "system_prompt":  "You are a helpful domestic appliance advisor for the ElectroHome company. Please answer customer questions and do not mention other brands. If you cannot answer please say so.", "max_new_tokens":  1000, "prompt_style":  "raw"}}
+      ]
     }
   ]
 }

src/training/prep_finetuning.py

@@ -180,7 +180,7 @@ def training_string_from_q_and_a(q: str, a: str, sys_prompt: str = None) -> str:
     Build the single llama formatted training string from a question
     answer pair
     """
-    return f'{{"prompt": "{q}", "completion": "{a}"}}'
+    return f'User: {q}\nBot: {a}'
 
 
 def fine_tuning_out_dir(out_model: str) -> str: