Update README.md

README.md

@@ -1,199 +1,113 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+datasets:
+- openslr/librispeech_asr
+- slprl/sTinyStories2
+- slprl/SpokenSwag
+base_model:
+- Qwen/Qwen2.5-0.5B
+pipeline_tag: audio-to-audio
 ---
 
 # Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+This is a Speech Lanaguage Model trained for generating speech contiuations over discrete [Hubert tokens](https://huggingface.co/slprl/mhubert-base-25hz).
 
 
 ## Model Details
 
 ### Model Description
+This is a Speech Lanaguage Model, introduced in "_Slamming_: Training a Speech Language Model on One GPU in a Day", focusing on efficient training. 
+It was fine-tuned from [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) over a vocabulary of 500 speech tokens extracted from 
+the 11-th layer of [mhubert-25hz](https://huggingface.co/slprl/mhubert-base-25hz).
 
-<!-- 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.
+The model was trained by next-token prediction over a subset of LibriSpeech, Libri-Light and a synthetic data 
+[sTinyStories](https://huggingface.co/datasets/slprl/sTinyStories). It was then trained with DPO over 
+[SpokenSwag](https://huggingface.co/datasets/slprl/SpokenSwag).
 
-- **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]
+- **Developed by:** [SLP-RL](https://huggingface.co/slprl)
+- **Model type:** SpeechLM
+- **License:** MIT
+- **Finetuned from model:** [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B)
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [https://github.com/slp-rl/slam](https://github.com/slp-rl/slam)
+- **Paper:** [Soon!]
+- **Demo:** [Link](https://pages.cs.huji.ac.il/adiyoss-lab/slamming/)
 
 ## 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]
+This is a base SpeechLM and as such can be used to generate contiuations for speech segments, or as base for further tuning. See the _slam_
+[codebase](https://github.com/slp-rl/slam) for more details on usage, and checkout the [demo page](https://pages.cs.huji.ac.il/adiyoss-lab/slamming/) for some generation examples
 
 ### Out-of-Scope Use
+This model was trained on curated speech datasets which contain mainly audio-books and stories, as such the outputs should not be treated as factual in any way.
 
-<!-- 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.
 
 ## How to Get Started with the Model
+We refer users to the official repository for full usage explainations - [github](https://github.com/slp-rl/slam).
 
-Use the code below to get started with the model.
-
-[More Information Needed]
 
 ## Training Details
+We highly encourage users to read the full [paper](), for full training details, a brief overview is provided below.
 
-### 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 Data
+This model was trained on a subset of [LibriSpeech](https://huggingface.co/datasets/openslr/librispeech_asr) train, 
+[Libri-Light](https://ai.meta.com/tools/libri-light/) and the synthetic dataset
+[sTinyStories](https://huggingface.co/datasets/slprl/sTinyStories) for the pre-training phase. It was also trained with DPO on the synthetic
+dataset [SpokenSwag](https://huggingface.co/datasets/slprl/SpokenSwag).
 
 ### Training Procedure
+This model was trained by next token prediction over several dataset, and then trained with DPO over [SpokenSwag](https://huggingface.co/datasets/slprl/SpokenSwag).
+Please refer to the [paper]() or [code](https://github.com/slp-rl/slam) for the full training recipes.
 
-<!-- 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 -->
+#### Preprocessing
+Speech tokens are extracted from the audio using [Hubert-25hz](https://huggingface.co/slprl/mhubert-base-25hz), and quantised using the 
+official kmeans released with the model in [textlesslib](https://github.com/facebookresearch/textlesslib/tree/main). Units are de-duplicated.
+We encourage you to explore the official repository for full details - [github](https://github.com/slp-rl/slam).
 
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
 
 ## Evaluation
+The paper provides full results, we do give here some results and also refer to the [demo page](https://pages.cs.huji.ac.il/adiyoss-lab/slamming/) to listen to some samples.
+
+| Model                                     | GPUs    | Params | Num Tokens    | sBLIMP ↑  | sStoryCloze ↑ | tStoryCloze ↑ | GenPPL ↓ | Auto-BLEU ↓ |
+|-------------------------------------------|---------|--------|---------------|-----------|---------------|---------------|----------|-------------|
+| **Speech only pre-training**              |         |        |               |           |               |               |          |             |
+| GSLM                                      | 8×V100  | 100M   | 1B            | 54.2      | 53.3          | 66.6          | —        | —           |
+| SyllableLM                                | 4×A40   | 300M   | 16B           | 63.7      | —             | 75.4          | —        | —           |
+| TWIST-350M                                | 8×V100  | 305M   | 10.8B         | 56.2      | —             | —             | 137.3    | 3.46        |
+| TWIST-1.3B                                | 32×V100 | 1B     | 10.8B         | 57.0      | 52.4          | 70.6          | 131.8    | 3.20        |
+| TWIST-7B                                  | 32×V100 | 7B     | 36B           | 59.0      | 55.3          | 74.1          | 93.74    | 3.06        |
+| TWIST-13B                                 | 32×V100 | 13B    | 36B           | 59.2      | 55.4          | 76.4          | —        | —           |
+| Scaled Optimal                            | —       | 823M   | 82B           | **61.3**  | 56.7          | 78.0          | —        | —           |
+| Moshi                                     | ?×H100  | 7B     | ?             | 58.9      | **58.7**      | **81.8**      | —        | —           |
+| SpiritLM                                  | 64×A100 | 7B     | 100B          | 58.0      | 54.8          | 72.9          | —        | —           |
+| **With text / preference optimization**   |         |        |               |           |               |               |          |             |
+| Scaling Interleaving                      | —       | 9B     | ~1T           | —         | **62.4**      | 82.9          | —        | —           |
+| Moshi                                     | ?×H100  | 7B     | ~720B         | 58.8      | 60.8          | 83.0          | —        | —           |
+| SpiritLM                                  | 64×A100 | 7B     | 100B          | 58.3      | 61.0          | 82.9          | —        | —           |
+| AlignSLM-1.3B                             | 64×A100 | 1B     | 10.8B + ~158B | 59.8      | 55.0          | 80.0          | —        | —           |
+| AlignSLM-7B                               | 64×A100 | 7B     | 36B + ~158B   | **62.3**  | 61.1          | **86.8**      | —        | —           |
+| **Ours (_Slam_)**                         |         |        |               |           |               |               |          |             |
+| _Slam_ (-DPO)                             | 2×A100  | 358M   | 16.7B         | 58.53     | 58.15         | 80.71         | 67.3     | 3.25        |
+| _Slam_                                    | 1×A5000 | 358M   | 1.4B + 5M     | 58.86     | 58.04         | 82.04         | 62.8     | 3.88        |
+| _Slam_ (scaled)                           | 2×A100  | 358M   | 16.7B + 9M    | **61.11** | **61.30**     | **84.18**     | **46.6** | 3.75        |
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- 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]
+This model was trained as part of ["*Slamming*: Training a Speech Language Model on One GPU in a Day"], focusing on efficient training.
 
 #### Hardware
-
-[More Information Needed]
+This model was trained using **only 2 Nvidia A100 GPU** for **48 hours**.
 
 #### Software
+The model was trained using the [*Slam*](https://github.com/slp-rl/slam) codebase which builds upon 🤗transformers extending it to support
+easy and efficent training of Speech Language Models.
 
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **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]
+Soon!