Daniel Marques commited on
Commit
fe3defb
1 Parent(s): 416e7fd

feat: add websocket

Browse files
Files changed (4) hide show
  1. constants.py +2 -2
  2. load_models.py +0 -2
  3. main.py +1 -0
  4. prompt_template_utils.py +6 -1
constants.py CHANGED
@@ -37,8 +37,8 @@ MAX_NEW_TOKENS = CONTEXT_WINDOW_SIZE # int(CONTEXT_WINDOW_SIZE/4)
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  #### If you get a "not enough space in the buffer" error, you should reduce the values below, start with half of the original values and keep halving the value until the error stops appearing
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- N_GPU_LAYERS = 40 # Llama-2-70B has 83 layers
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- N_BATCH = 1024
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  ### From experimenting with the Llama-2-7B-Chat-GGML model on 8GB VRAM, these values work:
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  # N_GPU_LAYERS = 20
 
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  #### If you get a "not enough space in the buffer" error, you should reduce the values below, start with half of the original values and keep halving the value until the error stops appearing
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+ N_GPU_LAYERS = 100 # Llama-2-70B has 83 layers
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+ N_BATCH = CONTEXT_WINDOW_SIZE
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  ### From experimenting with the Llama-2-7B-Chat-GGML model on 8GB VRAM, these values work:
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  # N_GPU_LAYERS = 20
load_models.py CHANGED
@@ -58,8 +58,6 @@ def load_quantized_model_gguf_ggml(model_id, model_basename, device_type, loggin
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  "model_path": model_path,
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  "n_ctx": CONTEXT_WINDOW_SIZE,
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  "max_tokens": MAX_NEW_TOKENS,
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- "n_batch": MAX_NEW_TOKENS,
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-
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  # set this based on your GPU & CPU RAM
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  }
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  if device_type.lower() == "mps":
 
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  "model_path": model_path,
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  "n_ctx": CONTEXT_WINDOW_SIZE,
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  "max_tokens": MAX_NEW_TOKENS,
 
 
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  # set this based on your GPU & CPU RAM
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  }
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  if device_type.lower() == "mps":
main.py CHANGED
@@ -51,6 +51,7 @@ QA = RetrievalQA.from_chain_type(
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  return_source_documents=SHOW_SOURCES,
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  chain_type_kwargs={
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  "prompt": prompt,
 
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  },
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  )
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  return_source_documents=SHOW_SOURCES,
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  chain_type_kwargs={
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  "prompt": prompt,
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+ "memory": memory
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  },
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  )
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prompt_template_utils.py CHANGED
@@ -6,6 +6,11 @@ This seems to have significant impact on the output of the LLM.
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  from langchain.memory import ConversationBufferMemory
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  from langchain.prompts import PromptTemplate
 
 
 
 
 
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  # this is specific to Llama-2.
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@@ -84,7 +89,7 @@ def get_prompt_template(system_prompt=system_prompt, promptTemplate_type=None, h
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  )
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  prompt = PromptTemplate(input_variables=["context", "question"], template=prompt_template)
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- memory = ConversationBufferMemory(input_key="question", memory_key="history")
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  return (
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  prompt,
 
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  from langchain.memory import ConversationBufferMemory
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  from langchain.prompts import PromptTemplate
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+ from langchain.memory.chat_message_histories import RedisChatMessageHistory
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+
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+ message_history = RedisChatMessageHistory(
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+ url="redis://localhost:6379/1", ttl=600, session_id="my-session"
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+ )
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  # this is specific to Llama-2.
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  )
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  prompt = PromptTemplate(input_variables=["context", "question"], template=prompt_template)
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+ memory = ConversationBufferMemory(input_key="question", memory_key="history", chat_memory=message_history)
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  return (
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  prompt,