User Feedback on NVIDIA's Llama 3.1 Nemotron 70B Instruct: Strengths and Limitations
18/10/2024 13:38:53
The recently launched Llama 3.1 Nemotron 70B Instruct by NVIDIA, the top-ranked model on our list, has provoked hot discussions in the AI community.
As usual, we decided to summarize what users think about the model.
Brief overview of the LLM
Llama 3.1 Nemotron 70B Instruct is designed to improve the helpfulness of AI-generated responses to user queries. It ranks first on three key automatic alignment benchmarks:
- Arena Hard: 85.0
- AlpacaEval 2 LC: 57.6
- GPT-4-Turbo MT-Bench: 8.98
The model was created using a technique called RLHF (Reinforcement Learning from Human Feedback), specifically the REINFORCE algorithm. It was trained on HelpSteer2-Preference prompts, using Llama-3.1-70B-Instruct as its starting point. One notable feature of this model is its ability to answer simple questions accurately without needing special prompting. It is available for use through the HuggingFace Transformers library, requiring at least two 80GB GPUs (NVIDIA Ampere or newer) and 150GB of free disk space.
User Feedback
Initially, AI enthusiasts had low expectations for the NVIDIA model and some were even considering ignoring NVIDIA's AI offerings. However, after testing it, there was a significant shift in users' opinions - they found its performance to be much better than expected.
Further testing on Misguided Attention prompts revealed a potential trade-off between improved alignment and reduced flexibility of the model. While the model may perform well on standard benchmarks, it might struggle with tasks requiring more adaptable or creative thinking. The model may be less adaptable or open to alternative viewpoints or interpretations. This could be an important consideration for users who need an AI model capable of handling a wide range of unpredictable or novel situations.
The model was mentioned as performing well on prompts that typically cause hallucinations in other top models like o1-preview and Claude 3.5 Sonnet.
NSFW Content: Users noted that there is a performance difference depending on the platform they use to run the model. The NVIDIA platform version is more censored than when run through other means like SillyTavern. When not on the NVIDIA platform, the model handles NSFW content well, comparable to or better than other models like New-Dawn-Llama-3.1-70B. The model shows promise for roleplay applications, with good performance in story cohesion and character adherence. It is recommended to run the model with at least Q3_K_M quantization and 16k context for good performance in roleplay scenarios. (Still, opinions are split, as usual 😎. Some comments highlight a potential limitation in creative or narrative-based tasks like roleplaying. The tendency to produce step-by-step lists suggests that while the model excels in structured, logical thinking, it might struggle with more free-form, creative writing tasks).
Multilingual Capabilities: Some comments highlight that the model performed exceptionally well on German and French prompts. The model excelled at generating dialog in French. Some users note the model's ability to adapt to different writing styles and tones.
AI professionals compare Llama 3.1 Nemotron 70B Instruct's performance favorably with that of Mistral Large 2, a known high-performing model. Particularly, the NVIDIA model excels in several key areas: it showed significant improvements in STEM tasks (science, technology, engineering, and mathematics-related problems), and is proficient at logical reasoning tasks requiring clear chains of thought and problem-solving. The model outperforms Qwen2.5-72B in most areas, demonstrating broad capabilities. Users noted that it handles mathematical tasks well, which are often challenging for language models.
Coding Performance: Yes, Llama 3.1 Nemotron 70B Instruct is not primarily designed for coding. The model was specifically tuned for performance in logical reasoning, mathematics, and specific benchmark tasks. While it may not excel in coding compared to specialized coding models, this aligns with its intended purpose, and users stressed the importance of evaluating the model based on its designed strengths rather than expecting high performance across all AI tasks. This underscores a crucial point in AI development: different models are optimized for different tasks, and understanding these specializations is key to both fair evaluation and effective use of AI tools.
