feat(week8): add ensemble-joshua assignment (API, Modal, agents); omit large RF model

week8/community_contributions/ensemble-joshua/pricer_service2.py

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+import modal
+from modal import App, Volume, Image
+
+
+app = modal.App("pricer-service")
+image = Image.debian_slim().pip_install("huggingface", "torch", "transformers", "bitsandbytes", "accelerate", "peft")
+
+secrets = [modal.Secret.from_name("hf-secret")]
+
+# Constants
+GPU = "T4"
+BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
+PROJECT_NAME = "pricer"
+HF_USER = "ed-donner" 
+RUN_NAME = "2024-09-13_13.04.39"
+PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}"
+REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36"
+FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}"
+CACHE_DIR = "/cache"
+
+
+MIN_CONTAINERS = 0
+
+QUESTION = "How much does this cost to the nearest dollar?"
+PREFIX = "Price is $"
+
+hf_cache_volume = Volume.from_name("hf-hub-cache", create_if_missing=True)
+
+@app.cls(
+    image=image.env({"HF_HUB_CACHE": CACHE_DIR}),
+    secrets=secrets, 
+    gpu=GPU, 
+    timeout=1800,
+    min_containers=MIN_CONTAINERS,
+    volumes={CACHE_DIR: hf_cache_volume}
+)
+class Pricer:
+
+    @modal.enter()
+    def setup(self):
+        import torch
+        from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
+        from peft import PeftModel
+        
+        # Quant Config
+        quant_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_compute_dtype=torch.bfloat16,
+            bnb_4bit_quant_type="nf4"
+        )
+
+        # Load model and tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+        self.tokenizer.padding_side = "right"
+        self.base_model = AutoModelForCausalLM.from_pretrained(
+            BASE_MODEL, 
+            quantization_config=quant_config,
+            device_map="auto"
+        )
+        self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_MODEL, revision=REVISION)
+
+    @modal.method()
+    def price(self, description: str) -> float:
+        import os
+        import re
+        import torch
+        from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
+        from peft import PeftModel
+    
+        set_seed(42)
+        prompt = f"{QUESTION}\n\n{description}\n\n{PREFIX}"
+        inputs = self.tokenizer.encode(prompt, return_tensors="pt").to("cuda")
+        attention_mask = torch.ones(inputs.shape, device="cuda")
+        outputs = self.fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1)
+        result = self.tokenizer.decode(outputs[0])
+    
+        contents = result.split("Price is $")[1]
+        contents = contents.replace(',','')
+        match = re.search(r"[-+]?\d*\.\d+|\d+", contents)
+        return float(match.group()) if match else 0
+
+
+# Simple HTTP endpoint so external apps can call this on Modal
+@app.function(image=image, secrets=secrets, gpu=GPU, timeout=1800)
+@modal.web_endpoint(method="POST")
+def price_http(body: dict):
+    """HTTP endpoint: {"description": str} -> {"price": float}"""
+    description = body.get("description", '').strip()
+    if not description:
+        return {"error": "Missing 'description'"}
+
+    pricer = Pricer()
+    value = pricer.price.remote(description)
+    return {"price": float(value)}
+
+