59 lines
2.0 KiB
Markdown
59 lines
2.0 KiB
Markdown
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---
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base_model:
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- LiquidAI/LFM2.5-1.2B-Base
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- lfm2
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license: apache-2.0
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language:
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- en
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datasets:
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- unsloth/alpaca-cleaned
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- uniquealexx/Kimi-K2.6-Thinking-200x
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- nvidia/Nemotron-Cascade-2-SFT-Data
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- iamtarun/python_code_instructions_18k_alpaca
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- Entity-27th/ODIN-C1-SFT-Mix
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- Entity-27th/ODIN-C1-Realign
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---
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ODIN-C1(**O**n-**D**evice accelerated **I**ntelligent **N**etwork-**C**ode **1**) is a sLM engineered specifically for on-device programming.
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Based on LFM architecture, ODIN-C1 provides various advantages over other models such as hardware-agnostic inference affinity.
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## Quick start
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```python
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from transformers import pipeline
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question = "Can you write a simple Python script that shows the Fibonacci sequence?"
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generator = pipeline("text-generation", model="SKIS-AI-Research/ODIN-C1", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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This model was trained with Supervised Fine-Tuning(SFT) on a single AMD Instinct MI300X accelerator and GeForce RTX 4070 Laptop GPU.
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## Citations
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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``
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This lfm2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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