wip

src/compute_horde_prompt_gen/model.py

@@ -78,7 +78,7 @@ def tokenize(prompt: str) -> str:
         return inputs
 
     def tokenize_phi3(self, prompts: list[str], role: str) -> str:
-        inputs = [{"role": role, "content": prompt} for prompt in prompts]
+        inputs = [{"role": "user", "content": prompt} for prompt in prompts]
         inputs = self.tokenizer.apply_chat_template(
             inputs, add_generation_prompt=True, return_tensors="pt"
         ).to("cuda")
@@ -99,10 +99,9 @@ def generate(
             inputs = self.tokenize_phi3(prompts, role)
         else:
             raise ValueError(f"Unknown model {self.model_name}")
-        print(inputs)
 
         return self.model.generate(
-            **inputs,
+            inputs,
             max_new_tokens=max_new_tokens,
             temperature=temperature,
             num_return_sequences=num_return_sequences,

src/compute_horde_prompt_gen/prompt.py

@@ -16,7 +16,7 @@ def generate_prompt(self) -> str:
         formats = self.random_select(FORMATS, num=5)
 
         prompt = (
-            f"Generate a list of 5 complex prompts (questions or instruct tasks) that cover a wide range of skills and knowledge areas related to the themes of {themes}. "
+            f"Generate a list of 5 concise prompts (questions or instruct tasks) that cover a wide range of skills and knowledge areas related to the themes of {themes}. "
             f"Each of these prompts should: "
             f"\n- have a complexity level of {complexity_level} out of 20 and a relevance level to the theme of {relevance_level} out of 20"
             f"\n- test various cognitive abilities ({abilities}) and require different types of writting formats ({formats})"

src/compute_horde_prompt_gen/run.py

@@ -42,6 +42,7 @@ def generate_prompts(
         new_prompts = []
         for j, sequence in enumerate(sequences):
             output = model.decode(sequence)
+            log.info(f"{i=} output={output}")
             generated_prompts = parse_output(output)
             log.debug(f"{i=} sequence={j} {generated_prompts=} from {output=}")