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llm_config.py
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# llm_config.py
LLM_TYPE = "ollama" # Options: 'llama_cpp', 'ollama', 'openai', 'anthropic'
# LLM settings for llama_cpp
MODEL_PATH = "/home/james/llama.cpp/models/gemma-2-9b-it-Q6_K.gguf" # Replace with your llama.cpp models filepath
LLM_CONFIG_LLAMA_CPP = {
"llm_type": "llama_cpp",
"model_path": MODEL_PATH,
"n_ctx": 20000, # context size
"n_gpu_layers": 0, # number of layers to offload to GPU (-1 for all, 0 for none)
"n_threads": 8, # number of threads to use
"temperature": 0.7, # temperature for sampling
"top_p": 0.9, # top p for sampling
"top_k": 40, # top k for sampling
"repeat_penalty": 1.1, # repeat penalty
"max_tokens": 1024, # max tokens to generate
"stop": ["User:", "\n\n"] # stop sequences
}
# LLM settings for Ollama
LLM_CONFIG_OLLAMA = {
"llm_type": "ollama",
"base_url": "http://localhost:11434", # default Ollama server URL
"model_name": "custom-phi3-32k-Q4_K_M", # Replace with your Ollama model name
"temperature": 0.7,
"top_p": 0.9,
"n_ctx": 55000,
"context_length": 55000,
"stop": ["User:", "\n\n"]
}
# LLM settings for OpenAI
LLM_CONFIG_OPENAI = {
"llm_type": "openai",
"api_key": "", # Set via environment variable OPENAI_API_KEY
"base_url": None, # Optional: Set to use alternative OpenAI-compatible endpoints
"model_name": "gpt-4o", # Required: Specify the model to use
"messages": [], # Placeholder for conversation history
"temperature": 0.7,
"top_p": 0.9,
"max_tokens": 32000,
"stop": ["User:", "\n\n"],
"presence_penalty": 0,
"frequency_penalty": 0
}
# LLM settings for Anthropic
LLM_CONFIG_ANTHROPIC = {
"llm_type": "anthropic",
"api_key": "", # Set via environment variable ANTHROPIC_API_KEY
"model_name": "claude-3-5-sonnet-latest", # Required: Specify the model to use
"temperature": 0.7,
"top_p": 0.9,
"max_tokens": 4096,
"stop": ["User:", "\n\n"]
}
def get_llm_config():
if LLM_TYPE == "llama_cpp":
return LLM_CONFIG_LLAMA_CPP
elif LLM_TYPE == "ollama":
return LLM_CONFIG_OLLAMA
elif LLM_TYPE == "openai":
return LLM_CONFIG_OPENAI
elif LLM_TYPE == "anthropic":
return LLM_CONFIG_ANTHROPIC
else:
raise ValueError(f"Invalid LLM_TYPE: {LLM_TYPE}")