Added optimal text generation params.

test-job-app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from smolagents import ToolCallingAgent
 import torch
 
 # Load the SmolLM model and tokenizer
@@ -10,7 +11,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
 
-def smol_lm_process(job_description, system_prompt):
+def smol_lm_jd_process(job_description, system_prompt):
     # System Prompt and job description
     prompt = f"""<|im_start|>system
 {system_prompt}<|im_end|>
@@ -19,7 +20,7 @@ def smol_lm_process(job_description, system_prompt):
 <|im_start|>assistant
 """
     inputs = tokenizer(prompt, return_tensors="pt").to(device)
-    output = model.generate(**inputs, max_new_tokens=512)
+    output = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.6, top_k=40, top_p=0.9, repetition_penalty=1.1)
     response = tokenizer.decode(output[0], skip_special_tokens=False)
     # Extract the assistant's response
     start_idx = response.find("<|im_start|>assistant")
@@ -29,12 +30,12 @@ def smol_lm_process(job_description, system_prompt):
 
 def process_job_description(company_name, company_url, job_description):
     # Step 2: Extract key qualifications, skills, and requirements
-    system_prompt_requirements = "Extract key qualifications, skills, and requirements from this job description. Output as bullet points. Remove benefits/salary and fluff. ONLY INCLUDE INFORMATION THAT TELLS THE USER WHAT SKILLS THE EMPLOYER SEEKS."
-    role_requirements = smol_lm_process(job_description, system_prompt_requirements)
+    system_prompt_requirements = "Extract key qualifications, skills, and requirements from this job description. Output as bullet points. Remove benefits/salary, bragging about the company, and other fluff not relevant to the skills, qualifications, and job requirements. ONLY INCLUDE INFORMATION THAT TELLS THE USER WHAT SKILLS THE EMPLOYER SEEKS."
+    role_requirements = smol_lm_jd_process(job_description, system_prompt_requirements)
 
     # Step 3: Create a concise summary of the job description
-    system_prompt_summary = "Create a concise 150-200 word summary of this job description. Remove company bragging and benefits information. FOCUS ON ASPECTS THAT POINT THE USER IN WHAT THE EMPLOYER WANTS FROM A CANDIDATE IN TERMS OF SKILLS, ACCOMPLISHMENTS, AND SUCH"
-    clean_job_description = smol_lm_process(job_description, system_prompt_summary)
+    system_prompt_summary = "Create a concise 150-200 word summary of this job description. Remove company bragging bragging about the company, and other fluff not relevant to the position and what is desired from the candidate. FOCUS ON ASPECTS THAT POINT THE USER IN WHAT THE EMPLOYER WANTS FROM A CANDIDATE IN TERMS OF SKILLS, ACCOMPLISHMENTS, AND SUCH"
+    clean_job_description = smol_lm_jd_process(job_description, system_prompt_summary)
 
     return {
         "Company Name": company_name,