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"""
test_constrained_model_spaces.py - SPACES-OPTIMIZED Constrained Generation
Ultra-aggressive optimization for Hugging Face Spaces environment
"""
import torch
import json
import jsonschema
from transformers import AutoTokenizer, AutoModelForCausalLM
from typing import Dict
import time
import threading
class TimeoutException(Exception):
pass
def load_trained_model():
"""Load our model - SPACES OPTIMIZED"""
print("🔄 Loading SmolLM3-3B Function-Calling Agent...")
base_model_name = "HuggingFaceTB/SmolLM3-3B"
try:
print("🔄 Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
print("🔄 Loading base model...")
# SPACES OPTIMIZED: Memory efficient loading
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.float16,
device_map="auto",
low_cpu_mem_usage=True
)
# Try multiple paths for fine-tuned adapter
adapter_paths = [
"jlov7/SmolLM3-Function-Calling-LoRA", # Hub (preferred)
"./model_files", # Local cleaned path
"./smollm3_robust", # Original training output
"./hub_upload", # Upload-ready files
]
model_loaded = False
for i, adapter_path in enumerate(adapter_paths):
try:
if i == 0:
print("🔄 Loading fine-tuned adapter from Hugging Face Hub...")
else:
print(f"🔄 Trying local path: {adapter_path}")
from peft import PeftModel
model = PeftModel.from_pretrained(model, adapter_path)
model = model.merge_and_unload()
if i == 0:
print("✅ Fine-tuned model loaded successfully from Hub!")
else:
print(f"✅ Fine-tuned model loaded successfully from {adapter_path}!")
model_loaded = True
break
except Exception as e:
if i == 0:
print(f"⚠️ Hub adapter not found: {e}")
else:
print(f"⚠️ Path {adapter_path} failed: {e}")
continue
if not model_loaded:
print("🔧 Using base model with optimized prompting")
print("✅ Model loaded successfully")
return model, tokenizer
except Exception as e:
print(f"❌ Error loading model: {e}")
raise
def constrained_json_generate(model, tokenizer, prompt: str, schema: Dict, max_attempts: int = 2):
"""SPACES-OPTIMIZED generation with aggressive timeouts"""
device = next(model.parameters()).device
for attempt in range(max_attempts):
try:
# VERY aggressive settings for Spaces
temperature = 0.1 + (attempt * 0.2) # Start low, increase if needed
inputs = tokenizer(prompt, return_tensors="pt").to(device)
# Use threading timeout (cross-platform)
result = [None]
error = [None]
def generate_with_timeout():
try:
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=25, # VERY short for Spaces
temperature=temperature,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
num_return_sequences=1,
use_cache=True,
repetition_penalty=1.2 # Strong repetition penalty
)
result[0] = outputs
except Exception as e:
error[0] = str(e)
# Start generation thread
thread = threading.Thread(target=generate_with_timeout)
thread.daemon = True
thread.start()
thread.join(timeout=4) # 4-second timeout
if thread.is_alive():
return "", False, f"Generation timed out (attempt {attempt + 1})"
if error[0]:
return "", False, f"Generation error: {error[0]}"
if result[0] is None:
return "", False, f"Generation failed (attempt {attempt + 1})"
outputs = result[0]
# Extract generated text
generated_ids = outputs[0][inputs['input_ids'].shape[1]:]
response = tokenizer.decode(generated_ids, skip_special_tokens=True).strip()
# Try to extract JSON from response
if "{" in response and "}" in response:
start = response.find("{")
bracket_count = 0
end = start
for i, char in enumerate(response[start:], start):
if char == "{":
bracket_count += 1
elif char == "}":
bracket_count -= 1
if bracket_count == 0:
end = i + 1
break
json_str = response[start:end]
else:
json_str = response
# Validate JSON and schema
try:
parsed = json.loads(json_str)
jsonschema.validate(parsed, schema)
return json_str, True, None
except (json.JSONDecodeError, jsonschema.ValidationError) as e:
if attempt == max_attempts - 1:
return json_str, False, f"JSON validation failed: {str(e)}"
continue
except Exception as e:
if attempt == max_attempts - 1:
return "", False, f"Generation error: {str(e)}"
continue
return "", False, "All generation attempts failed"
def create_json_schema(function_def: Dict) -> Dict:
"""Create JSON schema for function definition"""
return {
"type": "object",
"properties": {
"name": {
"type": "string",
"enum": [function_def["name"]]
},
"arguments": function_def["parameters"]
},
"required": ["name", "arguments"]
}
def create_test_schemas():
"""Create simplified test schemas"""
return {
"weather_forecast": {
"name": "get_weather_forecast",
"description": "Get weather forecast",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"days": {"type": "integer"}
},
"required": ["location", "days"]
}
}
}
# Test if running directly
if __name__ == "__main__":
print("🧪 Testing SPACES-optimized model...")
try:
model, tokenizer = load_trained_model()
test_schema = create_test_schemas()["weather_forecast"]
schema = create_json_schema(test_schema)
prompt = """<|im_start|>system
You are a helpful assistant that calls functions by responding with valid JSON when given a schema. Always respond with JSON function calls only, never prose.<|im_end|>
<schema>
{"name": "get_weather_forecast", "description": "Get weather forecast", "parameters": {"type": "object", "properties": {"location": {"type": "string"}, "days": {"type": "integer"}}, "required": ["location", "days"]}}
</schema>
<|im_start|>user
Get weather for Tokyo for 5 days<|im_end|>
<|im_start|>assistant
"""
result, success, error = constrained_json_generate(model, tokenizer, prompt, schema)
print(f"✅ Result: {result}")
print(f"✅ Success: {success}")
if error:
print(f"⚠️ Error: {error}")
except Exception as e:
print(f"❌ Test failed: {e}") |