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---
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
---
<p align="center">
<picture>
<img src="https://raw.githubusercontent.com/trillion-labs/.github/main/Tri-7B.png" alt="Tri-7B", style="width: 80%;">
</picture>
</p>
# 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.
<p align="center">
<img src="https://raw.githubusercontent.com/trillion-labs/.github/main/pareto-2507.png" alt="Average Performance vs. Approximate Training FLOPs" style="width: 100%; max-width: 1400px;">
</p>
### 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.
<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 |
| **Knowledge and Reasoning** | | | |
| • KMMLU | Korean | 5-shot (0-shot, CoT) | accuracy |
| • MMLU | English | 5-shot (0-shot, CoT) | 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.
</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 |
| --- | --- | --- | --- |
| 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 (53.51) | 48.09 | +3.65 |
| MMLU | 68.16 (74.67) | 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

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{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}
{%- if tools %}
{{- '<|im_start|>system
' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Trillion, created by TrillionLabs. You are a helpful assistant.' }}
{%- endif %}
{{- "
# Tools
You may call one or more functions to assist with the user query.
You are provided with function signatures within <tools></tools> XML tags:
<tools>" }}
{%- for tool in tools %}
{{- "
" }}
{{- tool | tojson }}
{%- endfor %}
{{- '
</tools>
For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call><|im_end|>
' }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system
' + messages[0]['content'] + '<|im_end|>
' }}
{%- else %}
{{- '<|im_start|>system
You are Trillion, created by TrillionLabs. You are a helpful assistant.<|im_end|>
' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '
' + message.content + '<|im_end|>' + '
' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '
' + message.content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '
<tool_call>
' }}
{%- if tool_call is mapping %}
{{- '{"name": "' + tool_call.name + '", "arguments": ' + tool_call.arguments | tojson + '}' -}}
{% else %}
{{- tool_call }}
{%- endif %}
{{- '
</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>
' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>tool' }}
{%- endif %}
{{- '
' + message.content }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>
' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant
' }}
{%- endif %}

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 0,
"eos_token_id": 128001,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 32768,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"pad_token_id": 128004,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 100000.0,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.53.2",
"use_cache": false,
"vocab_size": 128256
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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{
"_from_model_config": true,
"bos_token_id": 0,
"eos_token_id": 128001,
"pad_token_id": 128004,
"transformers_version": "4.53.2",
"use_cache": false
}

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