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* examples : add model conversion tool/example This commit adds an "example/tool" that is intended to help in the process of converting models to GGUF. Currently it supports normal causal models and embedding models. The readme contains instructions and command to guide through the process. The motivation for this to have a structured and repeatable process for model conversions and hopefully with time improve upon it to make the process easier and more reliable. We have started to use this for new model conversions internally and will continue doing so and improve it as we go along. Perhaps with time this should be placed in a different directory than the examples directory, but for now it seems like a good place to keep it while we are still developing it. * squash! examples : add model conversion tool/example Remove dependency on scikit-learn in model conversion example. * squash! examples : add model conversion tool/example Update transformer dep to use non-dev version. And also import `AutoModelForCausalLM` instead of `AutoModel` to ensure compatibility with the latest version. * squash! examples : add model conversion tool/example Remove the logits requirements file from the all requirements file.
100 lines
3.7 KiB
Python
Executable file
100 lines
3.7 KiB
Python
Executable file
#!/usr/bin/env python3
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import argparse
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import os
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import importlib
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from pathlib import Path
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
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import torch
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import numpy as np
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unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
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parser = argparse.ArgumentParser(description='Process model with specified path')
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parser.add_argument('--model-path', '-m', help='Path to the model')
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args = parser.parse_args()
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model_path = os.environ.get('MODEL_PATH', args.model_path)
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if model_path is None:
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parser.error("Model path must be specified either via --model-path argument or MODEL_PATH environment variable")
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config = AutoConfig.from_pretrained(model_path)
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print("Model type: ", config.model_type)
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print("Vocab size: ", config.vocab_size)
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print("Hidden size: ", config.hidden_size)
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print("Number of layers: ", config.num_hidden_layers)
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print("BOS token id: ", config.bos_token_id)
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print("EOS token id: ", config.eos_token_id)
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print("Loading model and tokenizer using AutoTokenizer:", model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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config = AutoConfig.from_pretrained(model_path)
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if unreleased_model_name:
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model_name_lower = unreleased_model_name.lower()
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unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
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class_name = f"{unreleased_model_name}ForCausalLM"
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print(f"Importing unreleased model module: {unreleased_module_path}")
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try:
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model_class = getattr(importlib.import_module(unreleased_module_path), class_name)
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model = model_class.from_pretrained(model_path) # Note: from_pretrained, not fromPretrained
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except (ImportError, AttributeError) as e:
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print(f"Failed to import or load model: {e}")
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exit(1)
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else:
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model = AutoModelForCausalLM.from_pretrained(model_path)
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model_name = os.path.basename(model_path)
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# Printing the Model class to allow for easier debugging. This can be useful
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# when working with models that have not been publicly released yet and this
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# migth require that the concrete class is imported and used directly instead
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# of using AutoModelForCausalLM.
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print(f"Model class: {model.__class__.__name__}")
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prompt = "Hello, my name is"
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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print(f"Input tokens: {input_ids}")
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print(f"Input text: {repr(prompt)}")
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print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")
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with torch.no_grad():
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outputs = model(input_ids)
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logits = outputs.logits
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# Extract logits for the last token (next token prediction)
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last_logits = logits[0, -1, :].cpu().numpy()
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print(f"Logits shape: {logits.shape}")
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print(f"Last token logits shape: {last_logits.shape}")
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print(f"Vocab size: {len(last_logits)}")
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data_dir = Path("data")
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data_dir.mkdir(exist_ok=True)
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bin_filename = data_dir / f"pytorch-{model_name}.bin"
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txt_filename = data_dir / f"pytorch-{model_name}.txt"
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# Save to file for comparison
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last_logits.astype(np.float32).tofile(bin_filename)
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# Also save as text file for easy inspection
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with open(txt_filename, "w") as f:
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for i, logit in enumerate(last_logits):
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f.write(f"{i}: {logit:.6f}\n")
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# Print some sample logits for quick verification
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print(f"First 10 logits: {last_logits[:10]}")
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print(f"Last 10 logits: {last_logits[-10:]}")
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# Show top 5 predicted tokens
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top_indices = np.argsort(last_logits)[-5:][::-1]
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print("Top 5 predictions:")
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for idx in top_indices:
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token = tokenizer.decode([idx])
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print(f" Token {idx} ({repr(token)}): {last_logits[idx]:.6f}")
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print(f"Saved bin logits to: {bin_filename}")
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print(f"Saved txt logist to: {txt_filename}")
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