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---
base_model: unsloth/DeepSeek-R1-Distill-Qwen-14B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Disclaimer!!
Hello! This model is not perfect yet, I am just experimenting!
This is me attempting the [AIMO Prize 2 Kaggle contest](https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2)
I have decided to release the models before the competition ends because I don't care about winning the contest as much!
My research fields are Medical Computing and Reinforcement Learning. Feel free to add me on [LinkedIn](https://www.linkedin.com/in/sindhusatish/) if you want to chat!
- **Developed by:** sindhusatish97
- **License:** apache-2.0
- **Finetuned from model :** unsloth/DeepSeek-R1-Distill-Qwen-14B
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. - Huge thanks to the awesome team for releasing these distilled models!
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# Test it out!
```python
!pip install unsloth
# Also get the latest nightly Unsloth!
!pip install --force-reinstall --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git
from unsloth import FastLanguageModel
import torch
max_seq_length = 5120 # I chose this value based on Qwen's max sequence length.
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "sindhusatish97/DeepSeek-R1-Distill-Qwen-14B-unsloth-bnb-4bit-AIMO_CoT",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
"""4 pints of a 5% antifreeze solution and 8 pints of a 20% antifreeze solution must be mixed to obtain 12 pints of a
solution with what percentage of antifreeze?"""
], return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_length = 2048)
``` |