trillionlabs
/

Trida-7B-Preview

@@ -1,199 +1,197 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## 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.
-## 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
-#### 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]
-#### 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. -->
-**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]

 ---
+license: apache-2.0
+tags:
+- finetuned
+- chat
+language:
+- en
+- ko
+- ja
+pipeline_tag: text-generation
 library_name: transformers
+extra_gated_fields:
+  Full Name: text
+  Email: text
+  Organization: text
 ---
+# Trida-7B-Preview
+## Introduction
+🚀 Trida-7B-Preview: Block Diffusion Language Model
+We introduce Trida-7B-Preview, a high-performance 7-billion parameter language model representing the first publicly released Block Diffusion Language Model to originate from Korea.
+### Model Overview
+Architecture: Block Diffusion Language Model
+Base Model: Continually pre-trained from the highly efficient Tri-7B model.
+Korean Language Leadership
+Trida-7B-Preview sets a new benchmark for generative models in the region. To our knowledge, it is the:
+- First Block Diffusion Language Model to be openly released in Korea.
+- Best-performing diffusion language model in Korean among similar model sizes.
+This model is a significant step forward for the Korean LLM community, demonstrating the effectiveness of the Block Diffusion paradigm for complex, multilingual tasks.
+### Key Highlights
+* **Block Diffusion Architecture**: Trida-7B-Preview leverages the **Block Diffusion** architecture, combining the strengths of **parallelized diffusion generation** with **autoregressive dependencies** for improved efficiency, control, and flexible-length sequence generation.
+* **Multilingual Leadership**: Specially optimized for **Korean, English, and Japanese**, offering robust performance across all three languages.
+* **Korean First**: To our knowledge, Trida-7B-Preview is the **first Block Diffusion Language Model** to be openly released in Korea.
+* **Best-in-Class Korean Performance**: It is the **best-performing diffusion language model in Korean** among models of similar size, setting a new benchmark for generative models in the region.
+### Model Specifications
+#### Trida-7B-Preview
+- Type: Block Diffusion Language Model
+- Training Stage: Pre-training & Post-training
+- Architecture: Transformer Decoder with RoPE, SwiGLU, RMSNorm
+- Number of Parameters: 7.76B
+- Number of Layers: 32
+- Number of Attention Heads: 32
+- Context Length: 4,096
+- Vocab Size: 128,256
+#### 🔄 Training and Methodology
+We followed the methodology outlined in the Fast-dLLM-v2 approach (as seen in the model: Efficient-Large-Model/Fast_dLLM_v2_7B [https://huggingface.co/Efficient-Large-Model/Fast_dLLM_v2_7B]).
+Continual Pre-training from Tri-7B:
+Trida-7B-Preview was continually pre-trained starting from our proprietary model, trillionlabs/Tri-7B. This process was executed using a Block Diffusion training paradigm to transition the efficient base model into a highly capable generative model.
+## 🚀 Quickstart
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "trillionlabs/Trida-7B-Preview"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+prompt = "Hey Trida. Why don'y you try that?"
+messages = [
+    {"role": "system", "content": "You are Trida, created by TrillionLabs. You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+# Fast-dLLM v2 style parallel decoding
+gen_ids = model.generate(
+    inputs["input_ids"],
+    tokenizer=tokenizer,
+    max_new_tokens=2048,
+    small_block_size=8,
+    threshold=0.9,
+)
+response = tokenizer.decode(
+    gen_ids[0][inputs["input_ids"].shape[1]:],
+    skip_special_tokens=True
+)
+print(response)
+```
+---
 ## Evaluation
+We evaluated Trida-7B-Preview across a comprehensive suite of benchmarks assessing general reasoning, knowledge recall, coding abilities, mathematical reasoning, and instruction-following capabilities.
+<details>
+<summary> Full evaluation settings </summary>
+| Benchmark | Language | Evaluation Setting | Metric |
+|:----------|:---------|:------------------|:-------|
+| **General Reasoning and Factuality** | | | |
+| • xwinograd_en | English | 0-shot | accuracy |
+| • xwinograd_jp | Japanese | 0-shot | accuracy |
+| • KoBEST | Korean | 5-shot | accuracy |
+| **Knowledge and Reasoning** | | | |
+| • KMMLU | Korean | 5-shot | accuracy |
+| • MMLU | English | 5-shot | accuracy |
+| • Global-MMLU-Lite-en | English | 5-shot | accuracy |
+| • Global-MMLU-Lite-ko | English | 5-shot | accuracy |
+| • Global-MMLU-Lite-ja | English | 5-shot | accuracy |
+| **Coding** | | | |
+| • HumanEval | English | 0-shot | pass@1 |
+| • MBPPPlus | English | 0-shot | pass@1 |
+| **Mathematical Reasoning** | | | |
+| • GSM8k | English | 0-shot, CoT | exact-match |
+| • KoGSM8k | Korean | 0-shot, CoT | exact-match |
+| • MATH500 | English | 0-shot, CoT | exact-match |
+| **Instruction Following and Chat** | | | |
+| • IFEval | English | 0-shot | strict-prompt |
+| • koIFEval | Korean | 0-shot | strict-prompt |
+</details>
+### Benchmark Results
+### General Reasoning and Factuality
+| Benchmark | Tria-7B-Preview |
+| --- | --- |
+| KoBEST | 74.08 |
+| KMMLU | 50.28 |
+| MMLU | 67.23 |
+| Global-MMLU-Lite-en | 73.5 |
+| Global-MMLU-Lite-ko | 64.25 |
+| xwinograd_en | 69.81 |
+| xwinograd_jp | 64.75 |
+### Coding
+| Benchmark | Tria-7B-Preview |
+| --- | --- |
+| HumanEval | 35.98 |
+| MBPPPlus | 42.59 |
+### Mathematical Reasoning
+| Benchmark | Trida-7B-Preview |
+| --- | --- |
+| GSM8k | 50.42 |
+| KoGSM8k | 51.18 |
+| MATH500 | 24.4 |
+### Instruction Following
+| Benchmark | Trida-7B-Preview |
+| --- | --- |
+| IFEval | 63.31 |
+| koIFEval | 68.6 |
+## Limitations
+- Language Support: The model is optimized for English, Korean, and Japanese. Usage with other languages may result in degraded performance.
+- Knowledge Cutoff: The model's information is limited to data available up to Febuary, 2025.
+## License
+This model is licensed under the Apache License 2.0.
+## Contact
+For inquiries, please contact: info@trillionlabs.co