model stuff

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "./llama2-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.33.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

@@ -0,0 +1,10 @@
+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "max_length": 4096,
+  "pad_token_id": 0,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.33.0.dev0"
+}

peftmerger.py

@@ -0,0 +1,21 @@
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+
+model_name = "./llama2-hf"
+adapters_name = './checkpoint-760/adapter_model'
+
+print(f"Starting to load the model {model_name} into memory")
+
+m = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    #load_in_4bit=True,
+    torch_dtype=torch.bfloat16,
+    #device_map={"": 0},
+)
+m = PeftModel.from_pretrained(m, adapters_name)
+m = m.merge_and_unload()
+
+print(f"Successfully loaded the model {model_name} into memory")
+
+m.save_pretrained("nyc-savvy")

pefttester.py

@@ -0,0 +1,59 @@
+## Taken from the QLoRA Guanaco demo on Gradio
+# https://github.com/artidoro/qlora
+# https://colab.research.google.com/drive/17XEqL1JcmVWjHkT-WczdYkJlNINacwG7?usp=sharing
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+
+import datetime
+import os
+from threading import Event, Thread
+from uuid import uuid4
+
+import requests
+
+
+model_name = './nyc-savvy'
+m = AutoModelForCausalLM.from_pretrained(model_name)
+
+if 'llama' in model_name or 'savvy' in model_name:
+    tok = LlamaTokenizer.from_pretrained(model_name)
+else:
+    tok = AutoTokenizer.from_pretrained(model_name)
+tok.bos_token_id = 1
+stop_token_ids = [0]
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        for stop_id in stop_token_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+stop = StopOnTokens()
+
+max_new_tokens = 1536
+
+messages = "A chat between a curious human and an assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
+messages += "### Human: What museums should I visit? - My kids are aged 12 and 5"
+messages += "### Assistant: "
+
+input_ids = tok(messages, return_tensors="pt").input_ids
+input_ids = input_ids.to(m.device)
+
+temperature = 0.7
+top_p = 0.9
+top_k = 0
+repetition_penalty = 1.1
+
+op = m.generate(
+    input_ids=input_ids,
+    max_new_tokens=100,
+    temperature=temperature,
+    do_sample=temperature > 0.0,
+    top_p=top_p,
+    top_k=top_k,
+    repetition_penalty=repetition_penalty,
+    stopping_criteria=StoppingCriteriaList([stop]),
+)
+for line in op:
+    print(tok.decode(line))

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