llama-3-neural-chat-v1-8b
Model Details
Model Description
I fine-tuned llama-3 8B on an approach similar to Intel's neural chat language model. I have slightly modified the data sources so it is stronger in coding, math, and writing. I use both SFT and DPO.
- Developed by: Locutusque
- Model type: Built with Meta Llama 3
- Language(s) (NLP): Many?
- License: Llama 3 license https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE
Quants
EXL2 @bartowski
GGUF @bartowski
Uses
This model has great performance in writing and coding.
Training Data
- Open-Orca/SlimOrca-Dedup
- jondurbin/airoboros-3.2
- microsoft/orca-math-word-problems-200k
- m-a-p/Code-Feedback
- MaziyarPanahi/WizardLM_evol_instruct_V2_196k
- mlabonne/orpo-dpo-mix-40k
Direct Use
Conversational AI.
Evaluations
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| truthfulqa_mc2 | 2 | none | 0 | acc | 0.5627 | ± | 0.0154 |
| gsm8k | 3 | strict-match | 5 | exact_match | 0.5481 | ± | 0.0137 |
| flexible-extract | 5 | exact_match | 0.5557 | ± | 0.0137 | ||
| agieval_nous | N/A | none | 0 | acc | 0.3763 | ± | 0.0093 |
| none | 0 | acc_norm | 0.3665 | ± | 0.0093 | ||
| - agieval_aqua_rat | 1 | none | 0 | acc | 0.2087 | ± | 0.0255 |
| none | 0 | acc_norm | 0.2047 | ± | 0.0254 | ||
| - agieval_logiqa_en | 1 | none | 0 | acc | 0.3456 | ± | 0.0187 |
| none | 0 | acc_norm | 0.3594 | ± | 0.0188 | ||
| - agieval_lsat_ar | 1 | none | 0 | acc | 0.1826 | ± | 0.0255 |
| none | 0 | acc_norm | 0.1783 | ± | 0.0253 | ||
| - agieval_lsat_lr | 1 | none | 0 | acc | 0.3549 | ± | 0.0212 |
| none | 0 | acc_norm | 0.3451 | ± | 0.0211 | ||
| - agieval_lsat_rc | 1 | none | 0 | acc | 0.5242 | ± | 0.0305 |
| none | 0 | acc_norm | 0.5130 | ± | 0.0305 | ||
| - agieval_sat_en | 1 | none | 0 | acc | 0.6650 | ± | 0.0330 |
| none | 0 | acc_norm | 0.6505 | ± | 0.0333 | ||
| - agieval_sat_en_without_passage | 1 | none | 0 | acc | 0.4175 | ± | 0.0344 |
| none | 0 | acc_norm | 0.3738 | ± | 0.0338 | ||
| - agieval_sat_math | 1 | none | 0 | acc | 0.4227 | ± | 0.0334 |
| none | 0 | acc_norm | 0.3682 | ± | 0.0326 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 66.50 |
| AI2 Reasoning Challenge (25-Shot) | 60.84 |
| HellaSwag (10-Shot) | 84.13 |
| MMLU (5-Shot) | 64.69 |
| TruthfulQA (0-shot) | 56.34 |
| Winogrande (5-shot) | 78.22 |
| GSM8k (5-shot) | 54.81 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard60.840
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.130
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.690
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard56.340
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard78.220
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard54.810