File size: 3,330 Bytes
f5ab8e5 bca55cf d5473c2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
license: creativeml-openrail-m
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
base_model:
- prithivMLmods/Triangulum-10B
pipeline_tag: text-generation
library_name: transformers
tags:
- triangulum_10b
- sft
- chain_of_thought
- ollama
- text-generation-inference
- llama_for_causal_lm
---
<pre align="center">
__ .__ .__
_/ |_ _______ |__|_____ ____ ____ __ __ | | __ __ _____
\ __\\_ __ \| |\__ \ / \ / ___\ | | \| | | | \ / \
| | | | \/| | / __ \_| | \/ /_/ >| | /| |__| | /| Y Y \
|__| |__| |__|(____ /|___| /\___ / |____/ |____/|____/ |__|_| /
\/ \//_____/ \/
</pre>
# **Triangulum 10B: Multilingual Large Language Models (LLMs)**
Triangulum 10B is a collection of pretrained and instruction-tuned generative models, designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.
# **Key Features**
- **Foundation Model**: Built upon LLaMA's autoregressive language model, leveraging an optimized transformer architecture for enhanced performance.
- **Instruction Tuning**: Includes supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align model outputs with human preferences for helpfulness and safety.
- **Multilingual Support**: Designed to handle multiple languages, ensuring broad applicability across diverse linguistic contexts.
# **Training Approach**
1. **Synthetic Datasets**: Utilizes long chain-of-thought synthetic data to enhance reasoning capabilities.
2. **Supervised Fine-Tuning (SFT)**: Aligns the model to specific tasks through curated datasets.
3. **Reinforcement Learning with Human Feedback (RLHF)**: Ensures the model adheres to human values and safety guidelines through iterative training processes.
# **How to use with transformers**
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import torch
from transformers import pipeline
model_id = "prithivMLmods/Triangulum-10B"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
# **Use Cases**
- Multilingual content generation
- Question answering and dialogue systems
- Text summarization and analysis
- Translation and localization tasks
# **Technical Details**
Triangulum 10B employs a state-of-the-art autoregressive architecture inspired by LLaMA. The optimized transformer framework ensures both efficiency and scalability, making it suitable for a variety of use cases.
|