v1.0

script.sh

+python scripts/convert_hf_checkpoint.py --checkpoint_dir  out/TinyLlama-1.1B-900B --model_name tiny_LLaMA_1b
+
+python test_weight.py --checkpoint_dir out/TinyLlama-1.1B-intermediate-900B
+
+
+python pretrain/tinyllama_code.py --devices 8 --train_data_dir data/code_specialist_python_java_javascript_c_go_8192
+
+
+
+python scripts/prepare_starcoder.py --source_path data/starcoderdata/ --tokenizer_path data/llama --destination_path data/code_specialist_python_java_javascript_c_go_8192 --split train --percentage 1.0 --filenames_subset ["python","cpp","go","java","javascript"] --chunk_size 4194816
+
+
+
+
+/data/TinyLlama/out/code_tiny_LLaMA_1b_python_java_go_cpp_javascript/iter-032000-ckpt.pth
+
+python scripts/convert_lit_checkpoint.py --out_dir /data/TinyLlama/out/tiny_LLaMA_1b/ --checkpoint_name iter-100000-ckpt.pth --model_name tiny_LLaMA_1b
\ No newline at end of file

sft/simple_inference.py

+from transformers import AutoTokenizer
+import transformers 
+import torch
+model = "PY007/TinyLlama-1.1B-Chat-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+prompt = "Give me detailed info about Jeo Biden."
+formatted_prompt = (
+    f"### Human: {prompt} ### Assistant:"
+)
+
+
+sequences = pipeline(
+    formatted_prompt,
+    do_sample=True,
+    top_k=50,
+    top_p = 0.9,
+    num_return_sequences=1,
+    repetition_penalty=1.1,
+    max_new_tokens=1024,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")