Hermes 3 Llama 3.2 3B GGUF
Hermes 3 Llama 3.2 3B GGUF is a powerful language model that offers advanced capabilities and improved performance. It's a generalist model that excels in tasks like roleplaying, reasoning, multi-turn conversation, and long context coherence. With its ChatML prompt format, it allows for more structured and interactive conversations. The model is also designed for function calling and structured outputs, making it a versatile tool for various applications. Its performance is competitive with other models in its class, with strengths in areas like logical deduction and reasoning. Overall, Hermes 3 Llama 3.2 3B GGUF is a reliable and efficient choice for users looking for a capable language model.
Table of Contents
Model Overview
The Hermes 3 - Llama-3.2 3B model is a cutting-edge language model that boasts impressive capabilities. It’s a generalist model, meaning it can handle a wide range of tasks and topics. But what makes it special?
Capabilities
Capable of generating both text and code, this model outperforms many open-source chat models across common industry benchmarks. Here are some of its key features:
- Advanced agentic capabilities: It can understand and respond to complex instructions and tasks.
- Improved roleplaying and reasoning: It can engage in multi-turn conversations, understand context, and make logical connections.
- Long context coherence: It can keep track of long conversations and maintain coherence throughout.
- Powerful steering capabilities: Users have control over the model’s responses and can guide the conversation.
Performance
This model shows remarkable performance in various tasks, with a balance of speed, accuracy, and efficiency. Let’s dive into the details!
Speed
The model’s training on H100s on LambdaLabs GPU Cloud has enabled it to process information quickly and efficiently. With a fine-tuned foundation model, it can handle a wide range of tasks with ease.
Accuracy
The model’s performance in various benchmarks is impressive, with high accuracy scores in tasks such as:
arc_challenge: 0.4411boolq: 0.8327hellaswag: 0.5453openbookqa: 0.3480piqa: 0.7639winogrande: 0.6590
These scores demonstrate the model’s ability to understand and process complex information accurately.
Efficiency
It is designed to be efficient, with a focus on aligning LLMs to the user. The model’s advanced agentic capabilities, roleplaying, reasoning, and multi-turn conversation abilities make it an excellent choice for a wide range of applications.
Limitations
While this model is powerful, it’s not perfect. Let’s explore some of its limitations.
Weaknesses in Specific Tasks
While it performs well in many areas, it struggles with certain tasks. For example, in the arc_challenge task, it achieves an accuracy of only 0.4411. Similarly, in the openbookqa task, its accuracy is 0.3480. These results indicate that it may not be the best choice for tasks that require high levels of logical reasoning or domain-specific knowledge.
Limited Contextual Understanding
It has a limited contextual understanding, which can lead to misinterpretation of user input. For instance, in the agieval_aqua_rat task, it achieves an accuracy of only 0.2283. This suggests that it may struggle to understand the nuances of human language and context.
Alternatives
If you’re looking for alternatives to this model, here are some options to consider:
Format
This model uses a transformer architecture and accepts input in the form of tokenized text sequences, utilizing ChatML as the prompt format. This format allows for a more structured system for engaging the LLM in multi-turn chat dialogue.
Supported Data Formats
- Tokenized text sequences
- ChatML prompts
Input Requirements
- System prompts allow steerability and control given to the end user
- User prompts can be formatted using the
tokenizer.apply_chat_template()method - Function calling requires a specific system prompt and pydantic model json schema
Output Requirements
- Responses can be generated in natural language or JSON format
- JSON format requires a specific system prompt and pydantic model json schema
Examples
Here are some examples of how to use this model:
Code Examples
- Formatting messages using the
tokenizer.apply_chat_template()method:
messages = [
{"role": "system", "content": "You are Hermes 3."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)
- Utilizing the prompt format without a system prompt:
messages = [
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)
- Function calling example:
system_prompt = "<|im_start|>system\nYou are a function calling AI model....\</tool_call><|im_end|>"
user_prompt = "<|im_start|>user\nFetch the stock fundamentals data for Tesla (TSLA)<|im_end|>"
model.generate(system_prompt + user_prompt)
- JSON mode example:
system_prompt = "<|im_start|>system\nYou are a helpful assistant that answers in JSON....\</schema><|im_end|>"
user_prompt = "<|im_start|>user\nGet the stock fundamentals data for Tesla (TSLA)<|im_end|>"
model.generate(system_prompt + user_prompt)
