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Yesterday  
Yesterday  
mention
@mattshumer
mattshumer
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 released 
 released 
resource
https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B
model
mattshumer/Reflection-Llama-3.1-70B
https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B
text
, an impressive model that achieved incredible results in benchmarks like MMLU. The model was fine-tuned using Reflection-Tuning and the dataset used wasn't released, but I created a small recipe with distilabel that allows generating a dataset with a similar output format:
, an impressive model that achieved incredible results in benchmarks like MMLU. The model was fine-tuned using Reflection-Tuning and the dataset used wasn't released, but I created a small recipe with distilabel that allows generating a dataset with a similar output format:
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1. We use MagPie 🐦  in combination with 
1. We use MagPie 🐦  in combination with 
link
https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct
https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct
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 to generate reasoning instructions.
 to generate reasoning instructions.
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2. We generate a response again using 
2. We generate a response again using 
link
https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct
https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct
text
, but we steer the LLM to generate an specific output format using a custom system prompt. In the system prompt, we instruct the LLM that it will have first to think 💭 and have reflections that will help resolving ambiguities. After that, we instruct the LLM to generate an output based on the previous thinking 
, but we steer the LLM to generate an specific output format using a custom system prompt. In the system prompt, we instruct the LLM that it will have first to think 💭 and have reflections that will help resolving ambiguities. After that, we instruct the LLM to generate an output based on the previous thinking 
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In this dataset 
In this dataset 
resource
https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning
dataset
gabrielmbmb/distilabel-reflection-tuning
https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning
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 you can found 5 rows that I generated with this recipe. You can also found the code of the pipeline in the file called 
 you can found 5 rows that I generated with this recipe. You can also found the code of the pipeline in the file called 
inline_code
`reflection.py`
reflection.py
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.
.
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Yesterday  @mattshumer released https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B, an impressive model that achieved incredible results in benchmarks like MMLU. The model was fine-tuned using Reflection-Tuning and the dataset used wasn't released, but I created a small recipe with distilabel that allows generating a dataset with a similar output format:

1. We use MagPie 🐦  in combination with https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct to generate reasoning instructions.
2. We generate a response again using https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct, but we steer the LLM to generate an specific output format using a custom system prompt. In the system prompt, we instruct the LLM that it will have first to think 💭 and have reflections that will help resolving ambiguities. After that, we instruct the LLM to generate an output based on the previous thinking 

In this dataset https://huggingface.co/datasets/gabrielmbmb/distilabel-reflection-tuning you can found 5 rows that I generated with this recipe. You can also found the code of the pipeline in the file called `reflection.py`.


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