Reflection-Llama-3.1-70B-Instruct-NF4

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This is a quantized version of Reflection Llama 3.1 70B Instruct. Quantized to 4-bit using bistandbytes and accelerate.

• Developed by: Farid Saud @ DSRS
• Base Model: meta-llama/Meta-Llama-3.1-70B-Instruct

[!WARNING]
There is (currently) a lot of controversy with this model's legitimacy, use with caution.

Use this model

Use a pipeline as a high-level helper:

# Use a pipeline as a high-level helper
from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="fsaudm/Reflection-Llama-3.1-70B-Instruct-NF4")
pipe(messages)

Load model directly

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fsaudm/Reflection-Llama-3.1-70B-Instruct-NF4")
model = AutoModelForCausalLM.from_pretrained("fsaudm/Reflection-Llama-3.1-70B-Instruct-NF4")

System Prompt

The system prompt used for training this model is:

You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.

We recommend using this exact system prompt to get the best results from Reflection 70B. You may also want to experiment combining this system prompt with your own custom instructions to customize the behavior of the model.

Chat Format

As mentioned above, the model uses the standard Llama 3.1 chat format. Here’s an example:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.<|eot_id|><|start_header_id|>user<|end_header_id|>

what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Tips for Performance

• We are initially recommending a temperature of .7 and a top_p of .95.
• For increased accuracy, append Think carefully. at the end of your messages.