LLM-Neo
Collection
Model hub for LLM-Neo, including Llama3.1-Neo-1B-100w and Minitron-4B-Depth-Neo-10w.
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We prune the Llama-3.1-8B-Instruct to 1.4B and fine-tune it with LLM-Neo method，which combines LoRA and KD in one. Training data is sampling from BAAI/Infinity-Instruct for 1 Million lines.
Benchmarks
In this section, we report the results for Llama3.1-Neo-1B-100w on standard automatic benchmarks. For all the evaluations, we use lm-evaluation-harness library.
Evaluation results
| Category | Benchmark | Version | n-shot | Metric | Value | Stderr |
| ARC | ARC-Challenge | 1 | 0 | acc | 0.1920 | ± 0.0115 |
| ARC-Easy | 1 | 0 | acc | 0.3834 | ± 0.0100 | |
| CEVAL | CEVAL (valid) | N/A | 0 | acc | 0.2370 | ± 0.0117 |
| CEVAL (Accountant) | 1 | 0 | acc | 0.2449 | ± 0.0621 | |
| CEVAL (Advanced Mathematics) | 1 | 0 | acc | 0.3158 | ± 0.1096 | |
| MMLU | MMLU | N/A | 0 | acc | 0.2439 | ± 0.0036 |
| MMLU (Abstract Algebra) | 0 | 0 | acc | 0.2500 | ± 0.0435 | |
| PIQA | PIQA | 1 | 0 | acc | 0.5843 | ± 0.0115 |
| PIQA (Normalized) | 1 | 0 | acc_norm | 0.5822 | ± 0.0115 | |
| Winogrande | Winogrande | 1 | 0 | acc | 0.5249 | ± 0.0140 |
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Model tree for yang31210999/Llama3.1-Neo-1B-100w
Base model
meta-llama/Llama-3.1-8B
Finetuned
meta-llama/Llama-3.1-8B-Instruct