QuantFactory/Llama-3.1-8B-Instruct-Reasoner-1o1_v0.3-GGUF

This is quantized version of Lyte/Llama-3.1-8B-Instruct-Reasoner-1o1_v0.3 created using llama.cpp

Original Model Card

Uploaded model

• NOTE: This model is just an experiment to make the model generate more tokens to do reasoning before providing an answer, with verifier and correction, this is just a proof of concept because literally, no model will show improvements in performance from such a tiny dataset(that doesn't target any specific knowledge) it may even degrade but the point wasn't to improve performance but to have it learn to "reason" because reaching SOTA in benchmarks does not equal "reasoning".
• Demo: try Q4_K_M here
• Developed by: Lyte
• License: apache-2.0
• Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit

Prompt

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

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

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{response}

Example(0-shot):

• the reason we keep seeing the correct word "strawberry" written again and again is simply a tokenizer issue. However, it did understand how to count correctly towards the end by saying, (the correct count is 4 'r's: one in "ar", three in "err"). The reason for "err" instead of "errr" is because of tokenization.

Benchmark Scores

• Note: Evals were ran with and without the system prompt that was used in the finetuning.

Task/Group	Metric	With Prompt	Without Prompt	Difference
arc_challenge	acc	51.37%	43.77%	+7.60%
	acc_norm	53.67%	46.42%	+7.25%
arc_easy	acc	81.99%	73.11%	+8.88%
	acc_norm	79.42%	64.98%	+14.44%
commonsense_qa	acc	76.00%	72.73%	+3.27%
gsm8k (flexible-extract)	exact_match	74.91%	76.57%	-1.66%
gsm8k (strict-match)	exact_match	73.92%	75.97%	-2.05%
hellaswag	acc	59.01%	58.87%	+0.14%
	acc_norm	77.98%	77.32%	+0.66%
mmlu (overall)	acc	66.06%	65.45%	+0.61%
mmlu - humanities	acc	61.47%	61.38%	+0.09%
mmlu - other	acc	72.84%	72.16%	+0.68%
mmlu - social sciences	acc	75.14%	73.94%	+1.20%
mmlu - stem	acc	57.37%	56.61%	+0.76%
piqa	acc	79.49%	78.45%	+1.04%
	acc_norm	80.47%	78.73%	+1.74%

Compared to the original Llama-3.1-8B-Instruct:

Task/Benchmark	Metric	Llama-3.1-8B-Instruct	Finetuned Model	Difference
MMLU	acc	69.40%	66.06%	-3.34%
ARC-Challenge	acc	83.40%	51.37%	-32.03%
CommonSenseQA	acc	75.00%*	76.00%	+1.00%
GSM-8K	exact_match	84.50%	74.91%	-9.59%

• Note: For Llama-3.1-8B-Instruct, the CommonSenseQA score is from the base model, not the instruct version. The -32.03% drop is very bad i have no idea if it's the finetuning that messed it up or difference in evals, but take it as you will, i did not plan to benchmark anything but oh well people won't stop asking to benchmark an experimental model(can you even properly benchmark the more tokens to do "reasoning"? i probably needed to adjust temperature to really make use of the model)...

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	25.05
IFEval (0-Shot)	70.98
BBH (3-Shot)	27.84
MATH Lvl 5 (4-Shot)	14.80
GPQA (0-shot)	2.68
MuSR (0-shot)	4.90
MMLU-PRO (5-shot)	29.09