Model Card: Locutusque/Hercules-6.1-Llama-3.1-8B
Model Description
Hercules-6.1-Llama-3.1-8B is a fine-tuned language model derived from Llama-3.1-8B. It is specifically designed to excel in instruction following, function calls, and conversational interactions across various scientific and technical domains. This fine-tuning has hercules-v6.1 with enhanced abilities in:
- Complex Instruction Following: Understanding and accurately executing multi-step instructions, even those involving specialized terminology.
- Function Calling: Seamlessly interpreting and executing function calls, providing appropriate input and output values.
- Domain-Specific Knowledge: Engaging in informative and educational conversations about Biology, Chemistry, Physics, Mathematics, Medicine, Computer Science, and more.
Intended Uses & Potential Bias
Hercules-6.1-Llama-3.1-8B is well-suited to the following applications:
- Specialized Chatbots: Creating knowledgeable chatbots and conversational agents in scientific and technical fields.
- Instructional Assistants: Supporting users with educational and step-by-step guidance in various disciplines.
- Code Generation and Execution: Facilitating code execution through function calls, aiding in software development and prototyping.
Important Note: Although Hercules-v6.1 is carefully constructed, it's important to be aware that the underlying data sources may contain biases or reflect harmful stereotypes. Use this model with caution and consider additional measures to mitigate potential biases in its responses.
Limitations and Risks
- Toxicity: The dataset contains toxic or harmful examples.
- Hallucinations and Factual Errors: Like other language models, Llama-3-Hercules-6.0-8B may generate incorrect or misleading information, especially in specialized domains where it lacks sufficient expertise.
- Potential for Misuse: The ability to engage in technical conversations and execute function calls could be misused for malicious purposes.
Evaluations
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | ||
|---|---|---|---|---|---|---|---|---|
| agieval_nous | 0.0 | none | acc | ↑ | 0.4427 | ± | 0.0094 | |
| - agieval_aqua_rat | 1.0 | none | 0 | acc | ↑ | 0.2913 | ± | 0.0286 |
| none | 0 | acc_norm | ↑ | 0.2480 | ± | 0.0272 | ||
| - agieval_logiqa_en | 1.0 | none | 0 | acc | ↑ | 0.3825 | ± | 0.0191 |
| none | 0 | acc_norm | ↑ | 0.3794 | ± | 0.0190 | ||
| - agieval_lsat_ar | 1.0 | none | 0 | acc | ↑ | 0.2087 | ± | 0.0269 |
| none | 0 | acc_norm | ↑ | 0.2043 | ± | 0.0266 | ||
| - agieval_lsat_lr | 1.0 | none | 0 | acc | ↑ | 0.4431 | ± | 0.0220 |
| none | 0 | acc_norm | ↑ | 0.4000 | ± | 0.0217 | ||
| - agieval_lsat_rc | 1.0 | none | 0 | acc | ↑ | 0.6097 | ± | 0.0298 |
| none | 0 | acc_norm | ↑ | 0.5428 | ± | 0.0304 | ||
| - agieval_sat_en | 1.0 | none | 0 | acc | ↑ | 0.7621 | ± | 0.0297 |
| none | 0 | acc_norm | ↑ | 0.6942 | ± | 0.0322 | ||
| - agieval_sat_en_without_passage | 1.0 | none | 0 | acc | ↑ | 0.4126 | ± | 0.0344 |
| none | 0 | acc_norm | ↑ | 0.3641 | ± | 0.0336 | ||
| - agieval_sat_math | 1.0 | none | 0 | acc | ↑ | 0.4318 | ± | 0.0335 |
| none | 0 | acc_norm | ↑ | 0.3500 | ± | 0.0322 | ||
| arc_challenge | 1.0 | none | 0 | acc | ↑ | 0.5247 | ± | 0.0146 |
| none | 0 | acc_norm | ↑ | 0.5606 | ± | 0.0145 | ||
| eq_bench | 2.1 | none | 0 | eqbench | ↑ | 63.2023 | ± | 2.6818 |
| none | 0 | percent_parseable | ↑ | 98.8304 | ± | 0.8246 | ||
| gsm8k | 3.0 | flexible-extract | 5 | exact_match | ↑ | 0.7801 | ± | 0.0114 |
| strict-match | 5 | exact_match | ↑ | 0.7809 | ± | 0.0114 | ||
| truthfulqa_mc2 | 2.0 | none | 0 | acc | ↑ | 0.5389 | ± | 0.0150 |
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 22.40 |
| IFEval (0-Shot) | 60.07 |
| BBH (3-Shot) | 24.15 |
| MATH Lvl 5 (4-Shot) | 15.63 |
| GPQA (0-shot) | 1.45 |
| MuSR (0-shot) | 3.42 |
| MMLU-PRO (5-shot) | 29.65 |