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README.md

@@ -1,199 +1,98 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
 ---
+# EvaByte Model Card
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+**EvaByte** is a 6.5B **byte-level language model** built upon an improved architecture with multibyte prediction and EVA -- an efficient attention mechanism designed for scalability and performance. Trained on 1.5T bytes spanning natural language text, math, and code, EvaByte demonstrates the viability of efficient byte-level processing at scale -- rivaling top open-source tokenizer-based LMs using 5x less training data, excelling in coding tasks, and decoding up to 2x faster.
 
+## Model Resources
 
+- **Repository:** https://github.com/openevabyte/evabyte
+- **Blog:** https://hkunlp.github.io/blog/2024/evabyte
+- **Paper:** Coming soon
 
 ## 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. -->
+EvaByte is trained using the SambaNova SN30 RDU system with a batch size of 8M bytes and 32K context length. The training process consists of 3 phases: after pre-training on 1.2T bytes (yielding **EvaByte-6.5B-Phase1**), two independent annealing runs (100B and 200B bytes respectively) are conducted with learning rate linearly decayed from 1e-4 to 0. The resulting checkpoints are merged via model soup (**EvaByte-6.5B**), which then undergoes supervised fine-tuning (**EvaByte-6.5B-SFT**).
+
+| Stage | Model |
+|:----- |:-----|
+| Base (before annealing) | [EvaByte-6.5B-Phase1](https://huggingface.co/evabyte/EvaByte-6.5B-Phase1) |
+| Base | [EvaByte-6.5B](https://huggingface.co/evabyte/EvaByte-6.5B) |
+| SFT  | [EvaByte-6.5B-SFT](https://huggingface.co/evabyte/EvaByte-6.5B-SFT) <-- you are here |
+
+
+## Usage
+
+Please note that we do not train the model with a specific system prompt during SFT.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("evabyte/EvaByte-6.5B-SFT", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("evabyte/EvaByte-6.5B-SFT", torch_dtype=torch.bfloat16, trust_remote_code=True).eval().to("cuda")
+
+messages = [
+    {"role": "user", "content": "Write me an English pangram."}
+]
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt", 
+).to("cuda")
+
+# byte-by-byte generation
+generation_output = model.generate(
+    input_ids=input_ids, 
+    max_new_tokens=256
+)
+# alternatively, use multibyte generation
+generation_output = model.multi_byte_generate(
+    input_ids=input_ids, 
+    max_new_tokens=256
+)
+
+response = tokenizer.decode(
+    generation_output[0][input_ids.shape[1]:], 
+    skip_special_tokens=False,
+    clean_up_tokenization_spaces=False
+)
+print(response)
+```
+We support two modes of generation:
+- `model.generate()`: When invoked, the model will generate one byte at a time. This is the default mode of generation with the Huggingface interface.
+- `model.multi_byte_generate()`: generate multiple bytes in a single step, adapted from the implementation of [Medusa](https://github.com/FasterDecoding/Medusa). This will be much faster than above and usually yields the same result under the setting of greedy decoding. `model.multi_byte_generate()` supports a subset of arguments in `model.generate()`:
+    - `input_ids`: the input byte ids.
+    - `temperature`: the temperature for sampling.
+    - `max_length`: the maximum length of the generated sequence.
+    - `max_new_tokens`: the maximum number of new bytes to generate.
+    - `stopping_criteria`: the [stopping criteria](https://huggingface.co/docs/transformers/v4.47.1/en/internal/generation_utils#transformers.StoppingCriteria) for generation.
+    - `top_p`: the top-p parameter for sampling.
+    - `do_sample`: greedy decoding or sampling.
+
+NOTE:
+- `device_map="auto"` is not supported for > 2 GPUs
+- Decoding only supports batch size of 1 with `attention_mask=None` for now.
+- Only supports `torch_dtype=torch.bfloat16` for now.
 
-[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
+`EvaByte-6.5B-SFT` serves primarily as a demonstration to showcase how the base model of EvaByte can be effectively fine-tuned for chat and instruction-following capabilities. While it shows improved conversational abilities, users should note that it has not undergone specific alignment or incorporated any moderation mechanisms. Like other instruction-tuned models without safety filtering, it can still generate potentially harmful, inappropriate, or factually incorrect content.
 
-<!-- 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
-
-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]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
+## Evaluation 
 
-#### Testing Data
+For detailed evaluation results, please refer to the [blog](https://hkunlp.github.io/blog/2024/evabyte).
 
-<!-- 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]
-
-<!-- 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]
+```
+@misc{evabyte,
+    title = {EvaByte: Efficient Byte-level Language Models at Scale},
+    url = {},
+    author = {Lin Zheng and Xueliang Zhao and Guangtao Wang and Chen Wu and David Dong and Angela Wang and Mingran Wang and Haige Bo and Tony Zhang and Changran Hu and Urmish Thakker and Lingpeng Kong},
+    year = {2025}
+}
+```