schuler
/

experimental-JP47D54

@@ -1,199 +1,85 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+license: mit
+datasets:
+- MBZUAI/LaMini-instruction
 ---
+# Saving 77% of the Parameters in Large Language Models Technical Report
+This repository contains experiment results for the [Saving 77% of the Parameters in Large Language Models Technical Report (PDF)](https://www.researchgate.net/publication/388835829_SAVING_77_OF_THE_PARAMETERS_IN_LARGE_LANGUAGE_MODELS_TECHNICAL_REPORT).
+## Abstract
+This technical report demonstrates that large language models (LLMs) can maintain their learning capacity while reducing their non-embedding parameters by up to 77%. We achieve this by adapting a parameter reduction technique originally developed for computer vision, replacing dense layers with an optimized subnetwork that contains grouped pointwise convolutions. Using Microsoft's phi-3-mini-4k-instruct as our baseline, we show that our optimized model (kphi-3) achieves comparable validation loss while using only 15-23% of the original non-embedding parameters. All experiments were conducted on a single NVIDIA L4 GPU within a 3-day timeframe, supporting the democratization of AI research. Our findings suggest that current LLM architectures may be substantially overparameterized, opening possibilities for more efficient model training and deployment.
+## Key Findings
+- Achieved 77% parameter reduction while maintaining model performance.
+- Demonstrated better generalization in optimized models.
+- Improved output quality in qualitative testing.
+## Implementation Details
+- Base Model: [kphi3](https://github.com/joaopauloschuler/less-parameters-llm).
+- Training Dataset: [LaMini](https://huggingface.co/datasets/MBZUAI/LaMini-instruction).
+- Architecture: Modified transformer decoder with grouped pointwise convolutions.
+## Results
+The following table shows LaMini training results with the baseline and the optimized versions. From left to right: experiment label, model name, number of transformer decoder layers, intermediate dimensions, number of non-embedding parameters, training loss and validation loss.
+| label | model | layers | interm. dims. | non-emb. params. | % | Train Loss | Val. Loss |
+|:-----:|:------:|:-------:|:-------------:|:----------------:|:---:|:----------:|:----------:|
+| [JP47D54C](https://github.com/joaopauloschuler/less-parameters-llm/tree/main/raw/JP47D54C_Baseline_2T.ipynb) | phi-3 | 2 | 8192 | [227M](https://huggingface.co/schuler/experimental-JP47D54C) | | **1.08** | 1.58 |
+| [JP47D55C](https://github.com/joaopauloschuler/less-parameters-llm/tree/main/raw/JP47D55C_kphi3_2T.ipynb) | kphi-3 | 2 | 9216 | [**35M**](https://huggingface.co/schuler/experimental-JP47D55C) | **15%** | 1.26 | 1.60 |
+| [JP47D56C](https://github.com/joaopauloschuler/less-parameters-llm/tree/main/raw/JP47D56C_kphi3_3T.ipynb) | kphi-3 | 3 | 9216 | [53M](https://huggingface.co/schuler/experimental-JP47D56C) | 23% | 1.21 | **1.57** |
+## Quick Links
+- [📄 Full Technical Report (PDF)](https://www.researchgate.net/publication/388835829_SAVING_77_OF_THE_PARAMETERS_IN_LARGE_LANGUAGE_MODELS_TECHNICAL_REPORT)
+- [🤗 Model Checkpoints on HuggingFace](https://huggingface.co/schuler/)
+- [📊 Raw Experiment Files](https://github.com/joaopauloschuler/less-parameters-llm/tree/main/raw)
+## Usage:
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig, pipeline
+from transformers import LlamaTokenizer
+import torch
+REPO_NAME = 'schuler/experimental-JP47D54C'
+def load_model(local_repo_name):
+    tokenizer = LlamaTokenizer.from_pretrained(local_repo_name, trust_remote_code=True)
+    generator_conf = GenerationConfig.from_pretrained(local_repo_name)
+    model = AutoModelForCausalLM.from_pretrained(local_repo_name, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="eager")
+    # model.to('cuda')
+    return tokenizer, generator_conf, model
+tokenizer, generator_conf, model = load_model(REPO_NAME)
+global_error = ''
+try:
+  generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+except Exception as e:
+  global_error =  f"Failed to load model: {str(e)}"
+def PrintTest(str):
+  print(generator(str, max_new_tokens=256, do_sample=True, top_p=0.25, repetition_penalty=1.2))
+PrintTest(f"<|user|>\nHello\n<|end|>\n<|assistant|>\n")
+PrintTest(f"<|user|>Hello\n<|end|><|assistant|>")
+PrintTest(f"<|user|>\nWhat is the human body?\n<|end|>\n<|assistant|>\n")
+PrintTest(f"<|user|>What is the human body?\n<|end|><|assistant|>")
+PrintTest(f"<|user|>What is biology?\n<|end|><|assistant|>")
+PrintTest(f"<|user|>Can you comment about democracy?\n<|end|><|assistant|>")
+PrintTest(f"<|user|>Can you provide detailed comments about the concept of democracy?\n<|end|><|assistant|>")
+PrintTest(f"<|user|>Please give me a detailed description of the python computer language.\n<|end|><|assistant|>")
+PrintTest(f"<|user|>If you had a difficult task to complete, how would you complete it?\n<|end|><|assistant|>")
+PrintTest(f"<|user|>Before replying to my question, I would like you to provide two candidate solutions and do some reflexion about these solution. Then, you'll pick the best as the final reply. What is best: eating healthy or eating economically?\n<|end|><|assistant|>")
+```
+## Output Examples
+```
+```
+## Citing this Model
+```
+@article{SchulerRojas_2025,
+  title={Saving 77% of the Parameters in Large Language Models Technical Report},
+  url={https://www.researchgate.net/publication/388835829_SAVING_77_OF_THE_PARAMETERS_IN_LARGE_LANGUAGE_MODELS_TECHNICAL_REPORT},
+  author={Schwarz Schuler, Joao Paulo and Rojas Gómez, Alejandra},
+  year={2025}}
+```