Update README.md

README.md

@@ -1,199 +1,57 @@
 ---
-library_name: transformers
-tags: []
+tags:
+- mamba2
+license: mit
 ---
 
-# Model Card for Model ID
+# mamba2-130m-av
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Introduction
+This is a mirror model to [mamba2-130m](https://huggingface.co/state-spaces/mamba2-130m) which is compatible with [mamba2-torch](https://github.com/vasqu/mamba2-torch), a Hugging Face compatible mamba2 library that is not dependent on the original cuda wheels of the [original mamba repo](https://github.com/state-spaces/mamba). Credit goes to the original authors of [Mamba2](https://arxiv.org/abs/2405.21060) and the [transformers](https://github.com/huggingface/transformers) library by Hugging Face. Without their work, this would not be possible.
 
 
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+NOTE: `mamba2-torch` offers differrent optimisation paths to use:
+- Triton kernels and [causal-conv1d](https://github.com/Dao-AILab/causal-conv1d) ("fastest")
+- Triton kernels only (default) 
+- Pure PyTorch
 
 ## How to Get Started with the Model
+You can follow the instructions in the [mamba2-torch repo](https://github.com/vasqu/mamba2-torch) for a more detailed explanation. First of all, you should install the mamba2-torch lib:
+```bash
+git clone https://github.com/vasqu/mamba2-torch.git
+cd mamba2-torch
+pip install .
+```
 
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+Then you can donwload this repository here via git lfs and then use the files locally the following way (after installing mamba2-torch):
+```python
+from transformers import AutoTokenizer
+from mamba2_torch import Mamba2Model, Mamba2ForCausalLM, Mamba2Config
 
-## Evaluation
+device = "cuda"
+mamba2_hf_path = "<path-to-converted-model>"
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+model = Mamba2ForCausalLM.from_pretrained(mamba2_hf_path, local_files_only=True).to(device)
+tokenizer = AutoTokenizer.from_pretrained(mamba2_hf_path, local_files_only=True)
 
-### Testing Data, Factors & Metrics
+input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"].to(device)
 
-#### Testing Data
+# expected output (130m): `["Hey how are you doing?\n\nI'm in the middle of a project"]`
+out = model.generate(input_ids, max_new_tokens=10)
+print(tokenizer.batch_decode(out))
+``` 
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+```bibtex
+@inproceedings{mamba2,
+ title={Transformers are {SSM}s: Generalized Models and Efficient Algorithms Through Structured State Space Duality},
+ author={Dao, Tri and Gu, Albert},
+ booktitle={International Conference on Machine Learning (ICML)},
+ year={2024}
+}
+```