Finetuned from model : Solshine/reflection-llama-3.1-8B-Solshine-trainround2-16bit
Inspired by and featuring the Reflection Tuning technique pioneered by Matt Shumer (possibly earlier innovated by the team at Anthropic.)
To the authors' knowledge, this is V4 of the first "reflection tuned" Llama 3.1 8B LLM
As per the inspiring model "mattshumer/Reflection-Llama-3.1-70B" (this mode was not used in the training process nor as a foundational model, but only served as inspiration) :
'''
During sampling, the model will start by outputting reasoning inside and tags, and then once it is satisfied with its reasoning, it will output the final answer inside tags. Each of these tags are special tokens, trained into the model.
This enables the model to separate its internal thoughts and reasoning from its final answer, improving the experience for the user.
Inside the section, the model may output one or more tags, which signals the model has caught an error in its reasoning and will attempt to correct it before providing a final answer.
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 tags, and then provide your final response inside
We recommend using this exact system prompt to get the best results from Reflection Llama-3.1 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:
You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside tags, and then provide your final response inside
what is 2+2?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
'''
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
bash
1brew install llama.cpp
2
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo Solshine/reflection-llama-3.1-8B-Solshine-trainround4-16bit-Q4_K_M-GGUF --hf-file reflection-llama-3.1-8b-solshine-trainround4-16bit-q4_k_m.gguf -p "The meaning to life and the universe is"
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo Solshine/reflection-llama-3.1-8B-Solshine-trainround4-16bit-Q4_K_M-GGUF --hf-file reflection-llama-3.1-8b-solshine-trainround4-16bit-q4_k_m.gguf -p "The meaning to life and the universe is"
