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mykor/
Tri-7B-gguf

Name: Tri-7B-gguf
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Tri-7B

Introduction

We introduce Tri-7B, the next generation model following Trillion-7B-preview, that continues to push the boundaries of efficient training while achieving exceptional performance at the 7B parameter scale.

Average Performance vs. Approximate Training FLOPs

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
Multi-lingual: Specially optimized for Korean, English, and Japanese.

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

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.

Full evaluation settings

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
Knowledge and Reasoning
• KMMLU	Korean	5-shot	accuracy
• MMLU	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
• 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)	LLM score
• KO-MT-Bench	Korean	LLM-as-a-judge (gpt-4o)	LLM score
• systemIFEval	English	0-shot	strict-average

*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.

Benchmark Results

Models compared:

Tri-7B (Next Generation)
Trillion-7B-preview (Previous Generation)

General Reasoning and Factuality

Benchmark	Tri-7B	Trillion-7B-preview	Improvement
HellaSwag	59.52	58.94	+0.58
ARC:C	58.28	54.44	+3.84
HAERAE	82.49	80.02	+2.47
KoBEST	82.72	79.61	+3.11
CLIcK	64.43	60.41	+4.02
KMMLU	51.74	48.09	+3.65
MMLU	68.16	63.52	+4.64
Global-MMLU-Lite-ja	59.25	60.75	-1.50

Coding

Benchmark	Tri-7B	Trillion-7B-preview	Improvement
HumanEval	53.66	55.48	-1.82
MBPPPlus	64.29	58.99	+5.30

Mathematical Reasoning

Benchmark	Tri-7B	Trillion-7B-preview	Improvement
GSM8k	77.94	72.25	+5.69
MATH	49.40	32.70	+16.70
GPQA	34.15	32.81	+1.34
HRM8k	39.08	30.10	+8.98

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
systemIFEval	66.43	27.28	+39.15

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

mykor/Tri-7B-gguf

Tri-7B

Introduction

Key Highlights

Model Specifications

Tri-7B

Quickstart

Tri-7B Usage

Evaluation

Benchmark Results

General Reasoning and Factuality

Coding

Mathematical Reasoning

Instruction Following and Chat

Limitations

License

Contact

mykor/
Tri-7B-gguf