Spaces:
Sleeping
Sleeping
File size: 11,774 Bytes
fc78ae4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 |
"""
Fallback model implementation for testing when llama-cpp-python is not available.
This provides a compatible model class that doesn't require any external dependencies,
allowing the rest of the application to function while we solve the llama-cpp-python
installation issues.
"""
import os
import logging
from typing import Dict, List, Optional, Any, Union
import requests
from smolagents import Model
from pathlib import Path
# Try to import llama_cpp, but don't fail if not available
try:
from llama_cpp import Llama
from pathlib import Path
LLAMA_CPP_AVAILABLE = True
except ImportError:
LLAMA_CPP_AVAILABLE = False
print("llama_cpp module not available, using fallback implementation")
logger = logging.getLogger(__name__)
class LlamaCppModel(Model):
"""Model using llama.cpp Python bindings for efficient local inference without PyTorch.
Falls back to a simple text generation if llama_cpp is not available."""
def __init__(
self,
model_path: str = None,
model_url: str = None,
n_ctx: int = 2048,
n_gpu_layers: int = 0,
max_tokens: int = 512,
temperature: float = 0.7,
verbose: bool = True
):
"""
Initialize a local llama.cpp model or fallback to a simple implementation.
Args:
model_path: Path to local GGUF model file
model_url: URL to download model if model_path doesn't exist
n_ctx: Context window size
n_gpu_layers: Number of layers to offload to GPU (0 means CPU only)
max_tokens: Maximum new tokens to generate
temperature: Sampling temperature
verbose: Whether to print verbose messages
"""
super().__init__()
self.model_path = model_path
self.model_url = model_url
self.n_ctx = n_ctx
self.max_tokens = max_tokens
self.temperature = temperature
self.verbose = verbose
self.llm = None
# Check if we can use llama_cpp
if LLAMA_CPP_AVAILABLE:
try:
if self.verbose:
print("Attempting to initialize LlamaCpp model...")
# Try to initialize the real model
if model_path and os.path.exists(model_path):
if self.verbose:
print(f"Loading model from {model_path}...")
# Initialize the llama-cpp model
self.llm = Llama(
model_path=model_path,
n_ctx=n_ctx,
n_gpu_layers=n_gpu_layers,
verbose=verbose
)
if self.verbose:
print("LlamaCpp model loaded successfully")
else:
if self.verbose:
print(f"Model path not found or not specified. Using fallback mode.")
except Exception as e:
logger.error(f"Error initializing LlamaCpp model: {e}")
if self.verbose:
print(f"Error initializing LlamaCpp model: {e}")
self.llm = None
else:
if self.verbose:
print("LlamaCpp not available, using fallback implementation")
if not self.llm and self.verbose:
print("Using fallback text generation mode")
def _resolve_model_path(self, model_path: str = None, model_url: str = None) -> str:
"""
Resolve model path, downloading if necessary.
Returns:
Absolute path to model file
"""
# Default to a small model if none specified
if not model_path:
models_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "models")
os.makedirs(models_dir, exist_ok=True)
model_path = os.path.join(models_dir, "ggml-model-q4_0.bin")
# Convert to Path for easier handling
path = Path(model_path)
# If model exists, return it
if path.exists():
return str(path.absolute())
# Download if URL provided
if model_url and not path.exists():
try:
print(f"Downloading model from {model_url}...")
os.makedirs(path.parent, exist_ok=True)
try:
# Try with streaming download first
with requests.get(model_url, stream=True, timeout=30) as r:
r.raise_for_status()
total_size = int(r.headers.get('content-length', 0))
block_size = 8192
with open(path, 'wb') as f:
downloaded = 0
for chunk in r.iter_content(chunk_size=block_size):
if chunk:
f.write(chunk)
downloaded += len(chunk)
if total_size > 0:
percent = (downloaded / total_size) * 100
if percent % 10 < (block_size / total_size) * 100:
print(f"Download progress: {int(percent)}%")
except (requests.exceptions.Timeout, requests.exceptions.ConnectionError) as e:
print(f"Streaming download timed out: {e}. Using a simpler approach...")
# Fall back to simpler download method
r = requests.get(model_url, timeout=60)
r.raise_for_status()
with open(path, 'wb') as f:
f.write(r.content)
print("Download complete with simple method")
print(f"Model download complete: {path}")
return str(path.absolute())
except Exception as e:
logger.error(f"Error downloading model: {e}")
print(f"Error downloading model: {e}")
print("Continuing with dummy model instead...")
# Create a small dummy model file so we can continue
with open(path, 'wb') as f:
f.write(b"DUMMY MODEL")
return str(path.absolute())
# If we get here without a model, create a dummy one
print(f"Model file not found at {model_path} and no URL provided. Creating dummy model...")
os.makedirs(path.parent, exist_ok=True)
with open(path, 'wb') as f:
f.write(b"DUMMY MODEL")
return str(path.absolute())
def generate(self, prompt: str, **kwargs) -> str:
"""
Generate text completion for the given prompt.
Args:
prompt: Input text
Returns:
Generated text completion
"""
try:
if self.verbose:
print(f"Generating with prompt: {prompt[:50]}...")
# If we have a real model, use it
if self.llm:
# Actual generation with llama-cpp
response = self.llm(
prompt=prompt,
max_tokens=self.max_tokens,
temperature=self.temperature,
echo=False # Don't include the prompt in the response
)
# Extract generated text
if not response:
return ""
if isinstance(response, dict):
generated_text = response.get('choices', [{}])[0].get('text', '')
else:
# List of responses
generated_text = response[0].get('text', '')
return generated_text.strip()
else:
# Fallback simple generation
if self.verbose:
print("Using fallback text generation")
# Extract key information from prompt
words = prompt.strip().split()
last_words = ' '.join(words[-10:]) if len(words) > 10 else prompt
# Simple response generation based on prompt content
if "?" in prompt:
return f"Based on the information provided, I believe the answer is related to {last_words}. This is a fallback response as the LLM model could not be loaded."
else:
return f"I understand you're asking about {last_words}. Since I'm running in fallback mode without a proper language model, I can only acknowledge your query but not provide a detailed response."
except Exception as e:
logger.error(f"Error generating text: {e}")
if self.verbose:
print(f"Error generating text: {e}")
return f"Error generating response: {str(e)}"
def generate_with_tools(
self,
messages: List[Dict[str, Any]],
tools: Optional[List[Dict[str, Any]]] = None,
**kwargs
) -> Dict[str, Any]:
"""
Generate a response with tool-calling capabilities.
This method implements the smolagents Model interface for tool-calling.
Args:
messages: List of message objects with role and content
tools: List of tool definitions
Returns:
Response with message and optional tool calls
"""
try:
# Format messages into a prompt
prompt = self._format_messages_to_prompt(messages, tools)
# Generate response
completion = self.generate(prompt)
# For now, just return the text without tool parsing
return {
"message": {
"role": "assistant",
"content": completion
}
}
except Exception as e:
logger.error(f"Error generating with tools: {e}")
print(f"Error generating with tools: {e}")
return {
"message": {
"role": "assistant",
"content": f"Error: {str(e)}"
}
}
def _format_messages_to_prompt(
self,
messages: List[Dict[str, Any]],
tools: Optional[List[Dict[str, Any]]] = None
) -> str:
"""Format chat messages into a text prompt for the model."""
formatted_prompt = ""
# Include tool descriptions if available
if tools and len(tools) > 0:
tool_descriptions = "\n".join([
f"Tool {i+1}: {tool['name']} - {tool['description']}"
for i, tool in enumerate(tools)
])
formatted_prompt += f"Available tools:\n{tool_descriptions}\n\n"
# Add conversation history
for msg in messages:
role = msg.get("role", "")
content = msg.get("content", "")
if role == "system":
formatted_prompt += f"System: {content}\n\n"
elif role == "user":
formatted_prompt += f"User: {content}\n\n"
elif role == "assistant":
formatted_prompt += f"Assistant: {content}\n\n"
# Add final prompt for assistant
formatted_prompt += "Assistant: "
return formatted_prompt |