Update README.md

README.md

@@ -1,67 +1,27 @@
 ---
-license: llama3.2
-base_model: meta-llama/Llama-3.2-3B-Instruct
-tags:
-- alignment-handbook
-- generated_from_trainer
-datasets:
-- JunxiongWang/sftdatasetv3
-model-index:
-- name: Llama-Mamba-3.2-3B-teacher-Llama-3.1-70B-Instruct-kl1.0-ce0.0-update
-  results: []
+license: apache-2.0
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/together-research/huggingface/runs/qfrd2pha)
-# Llama-Mamba-3.2-3B-teacher-Llama-3.1-70B-Instruct-kl1.0-ce0.0-update
-
-This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the JunxiongWang/sftdatasetv3 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 375.7168
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 4
-- eval_batch_size: 4
-- seed: 42
-- distributed_type: multi-GPU
-- num_devices: 8
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 64
-- total_eval_batch_size: 32
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.01
-- num_epochs: 1
-
-### Training results
-
-| Training Loss | Epoch  | Step  | Validation Loss |
-|:-------------:|:------:|:-----:|:---------------:|
-| 329.7069      | 1.0000 | 51995 | 375.7168        |
-
-
-### Framework versions
-
-- Transformers 4.43.1
-- Pytorch 2.1.1+cu118
-- Datasets 3.1.0
-- Tokenizers 0.19.1
+Zero-shot results when using the [Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) as the teacher model, and the [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) as the initialized model
+
+| Task          | Llama-3.2-3B-Instruct | Llama3.2-Mamba-3B-distill |
+|---------------|------------------------|--------------------------|
+| arc_challenge | 0.459                 | 0.4838                   |
+| arc_easy      | 0.7407                | 0.7765                   |
+| hellaswag     | 0.7043                | 0.7037                   |
+| mmlu          | 0.6043                | 0.5448                   |
+| openbookqa    | 0.36                  | 0.394                    |
+| piqa          | 0.7568                | 0.7731                   |
+| pubmedqa      | 0.696                 | 0.664                    |
+| race          | 0.4067                | 0.4029                   |
+| winogrande    | 0.6748                | 0.6732                   |
+
+
+```
+@article{junxiongdaniele2024mambainllama,
+  title   = {The Mamba in the Llama: Distilling and Accelerating Hybrid Models},
+  author  = {Junxiong Wang and Daniele Paliotta and Avner May and Alexander M. Rush and Tri Dao},
+  journal = {arXiv preprint arXiv:2408.15237},
+  year    = {2024}
+}
+```