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 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/Signiture_Black_White_BG_resized.jpg filter=lfs diff=lfs merge=lfs -text
+assets/Signiture_Trillion_BlackBG.png filter=lfs diff=lfs merge=lfs -text
+assets/Signiture_Trillion_WhiteBG.png filter=lfs diff=lfs merge=lfs -text

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README.md

@@ -1,199 +1,209 @@
 ---
+license: apache-2.0
+tags:
+- finetuned
+- chat
+language:
+- en
+- ko
+- ja
+pipeline_tag: text-generation
 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]
+# Tri-7B
+
+<p align="center">
+<picture>
+  <img src="assets/Signiture_Black_White_BG_resized.jpg" alt="logo", width="300", style="margin: 40 auto;">
+</picture>
+
+## Introduction
+
+We introduce **Tri-7B**, the next generation model following Trillion-7B-preview, designed to achieve exceptional performance with efficient training at the 7B parameter scale.
+
+### Key Highlights
+* **Enhanced Reasoning**: Modified training dataset mixture specifically optimized for reasoning capabilities
+* **Advanced Post-Training**: Significantly improved RL training pipeline focusing on mathematical reasoning and everyday usage
+* **Extended Context**: Supports up to 32K context length for long-form understanding
+
+Our **Tri-7B** model represents a significant advancement over Trillion-7B-preview, achieving substantial performance improvements across all evaluated domains while maintaining the same efficient parameter count.
+
+### Model Specifications
+
+#### Tri-7B
+- Type: Causal 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: 32,768
+- Vocab Size: 128,256
+
+
+## Quickstart
+
+Here is a code snippet with `apply_chat_template` that demonstrates how to load the tokenizer and model and generate text.
+
+### Tri-7B Usage
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "trillionlabs/Tri-7B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain the concept of quantum computing in simple terms."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+Tri-7B is also available with vLLM and SGLang!
+
+```bash
+# vLLM
+vllm serve trillionlabs/Tri-7B --dtype bfloat16 --max-model-len 32768
+
+# vLLM with custom options
+vllm serve trillionlabs/Tri-7B \
+    --dtype bfloat16 \
+    --max-model-len 32768 \
+    --gpu-memory-utilization 0.95 \
+    --port 8000
+```
+
+```bash
+# SGLang
+python3 -m sglang.launch_server --model-path trillionlabs/Tri-7B --dtype bfloat16
+
+# SGLang with custom options
+python3 -m sglang.launch_server \
+    --model-path trillionlabs/Tri-7B \
+    --dtype bfloat16 \
+    --context-length 32768 \
+    --port 30000 \
+    --host 0.0.0.0
+```
 
 ## 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]
+We evaluated Tri-7B across a comprehensive suite of benchmarks assessing general reasoning, knowledge recall, coding abilities, mathematical reasoning, and instruction-following capabilities. Compared to our previous generation model Trillion-7B-preview, Tri-7B achieves significant gains across all domains.
+
+<details>
+<summary> Full evaluation settings </summary>
+
+| Benchmark | Language | Evaluation Setting | Metric |
+|:----------|:---------|:------------------|:-------|
+| **General Reasoning and Factuality** | | | |
+| • HellaSwag | English | 0-shot | accuracy |
+| • ARC:C | English | 0-shot | accuracy |
+| • HAERAE | Korean | 3-shot | accuracy |
+| • CLIcK | Korean | 0-shot | accuracy |
+| • KoBEST | Korean | 5-shot | accuracy |
+| • BBH | English | 0-shot, CoT | accuracy |
+| **Knowledge and Reasoning** | | | |
+| • KMMLU | Korean | 5-shot | accuracy |
+| • MMLU | 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 |
+| • MATH | English | 0-shot, CoT | exact-match |
+| • GPQA | English | 4-shot | accuracy |
+| • HRM8k | Korean | 0-shot, CoT | exact-match |
+| **Instruction Following and Chat** | | | |
+| • IFEval | English | 0-shot | strict-average |
+| • koIFEval | Korean | 0-shot | strict-average |
+| • MT-Bench | English | LLM-as-a-judge (gpt-4o-2024-08-06) | LLM score |
+| • KO-MT-Bench | Korean | LLM-as-a-judge (gpt-4o-2024-08-06) | LLM score |
+| • LogicKor | Korean | LLM-as-a-judge (gpt-4o-2024-08-06) | LLM score |
+| • systemIFEval | English | 0-shot | strict-average |
+| **Long Context** | | | |
+| • KoRuler4K | Korean | 0-shot | exact-match |
+| • KoRuler8K | Korean | 0-shot | exact-match |
+| • Ruler4K | English | 0-shot | exact-match |
+| • Ruler8K | English | 0-shot | exact-match |
+
+- *Note that koIFEval, systemIFEval, and KoRuler are our in-house evaluation benchmarks adapted for Korean to better assess model capabilities in Korean language tasks.
+- **Note that MT-Bench, KO-MT-Bench, and LogicKor use a 10-point scale.
+
+</details>
+
+### Benchmark Results
+
+Models compared:
+- **Tri-7B** (Next Generation)
+- **Trillion-7B-preview** (Previous Generation)
+
+### General Reasoning and Factuality
+
+| Benchmark | Tri-7B | Trillion-7B-preview | Improvement |
+| --- | --- | --- | --- |
+| HAERAE | 82.49 | 80.02 | +2.47 |
+| KoBEST | 82.72 | 79.61 | +3.11 |
+| CLIcK | 64.43 | - | - |
+| KMMLU | 51.74 | 48.09 | +3.65 |
+| MMLU | 68.16 | 63.52 | +4.64 |
+
+### Coding
+
+| Benchmark | Tri-7B | Trillion-7B-preview | Improvement |
+| --- | --- | --- | --- |
+| HumanEval | 53.66 | 55.48 | -1.82 |
+| MBPPPlus | 64.29 | - | - |
+
+### Mathematical Reasoning
+
+| Benchmark | Tri-7B | Trillion-7B-preview | Improvement |
+| --- | --- | --- | --- |
+| GSM8k | 77.94 | 72.25 | +5.69 |
+| MATH | 49.40 | 32.70 | +16.70 |
+
+### Instruction Following and Chat
+
+| Benchmark | Tri-7B | Trillion-7B-preview | Improvement |
+| --- | --- | --- | --- |
+| IFEval | 79.26 | 79.13 | +0.13 |
+| koIFEval | 76.63 | 66.58 | +10.05 |
+| MT-Bench | 7.82 | 6.53 | +1.29 |
+| KO-MT-Bench | 7.64 | 6.27 | +1.37 |
+| LogicKor | 8.20 | 8.14 | +0.06 |
+| systemIFEval | 66.43 | - | - |
+
+## 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