File size: 3,051 Bytes
03941e4 be96b86 03941e4 be96b86 03941e4 be96b86 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 |
---
license: apache-2.0
language: en
datasets:
- lmms-lab/Math10K
base_model:
- Qwen/Qwen3-1.7B-Base
---
# NdLinear-LoRA Fine-Tuned Models
This repository contains a collection of language models fine-tuned using a custom NdLinear-LoRA architecture. NdLinear-LoRA is a variant of Low-Rank Adaptation (LoRA) that reshapes weight matrices into N-dimensional tensors and applies a factorized linear transformation for parameter-efficient fine-tuning.
## Available Models
Below is a list of the fine-tuned models. For best results, it's recommended to host each model in its own repository on the Hugging Face Hub.
| Fine-Tuned Model Name | Base Model | Fine-Tuning Dataset |
| ------------------------------------------------ | -------------------------- | ------------------- |
| `Meta-Llama-3-8B-CSQA-NdLinearLoRA` | `meta-llama/Llama-3-8B` | `commonsense_qa` |
| `Meta-Llama-3-8B-Math10K-NdLinearLoRA` | `meta-llama/Llama-3-8B` | `lmms-lab/Math10K` |
| `Qwen3-1.7B-CSQA-NdLinearLoRA` | `Qwen/Qwen3-1.7B-Base` | `commonsense_qa` |
| `Qwen3-1.7B-Math10K-NdLinearLoRA` | `Qwen/Qwen3-1.7B-Base` | `lmms-lab/Math10K` |
## How to Use
Because these models use a custom architecture, you must pass `trust_remote_code=True` when loading them. This allows the `transformers` library to download and use the `modeling_ndlinear.py` file that should be included in each model's repository.
**Dependencies:** Before you start, make sure you have the necessary libraries installed:
```bash
pip install torch transformers safetensors huggingface_hub accelerate
pip install ndlinear
```
### Example Loading Script
This script will work for any of the models listed above. Just change the `REPO_ID`.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# --- Example Usage ---
# 1. Choose the model you want to use from the table above
# Replace "YourUsername" with your Hugging Face username or organization.
REPO_ID = "YourUsername/Qwen3-1.7B-Math10K-NdLinearLoRA"
# 2. Load the model and tokenizer
# `trust_remote_code=True` is required to load the custom architecture.
print(f"Loading model: {REPO_ID}")
model = AutoModelForCausalLM.from_pretrained(
REPO_ID,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
print("Model and tokenizer loaded successfully.")
# 3. Generate text
# This prompt is geared for a math model. Adjust it for a QA model if needed.
prompt = "### Instruction:\\nSolve the following math problem: If a train travels at 60 miles per hour, how long does it take to travel 180 miles?\\n\\n### Solution:\\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=150, eos_token_id=tokenizer.eos_token_id)
print("\\n--- Generated Output ---")
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
``` |