Joe57005/Ramius3B-v1

Ramius

This is Ramius, an uncensored function calling model.

Model Description

I needed an LLM for Home Assistant that is small and performant and I wanted one with some personality.
Qwen2.5-3B is small, fast and can call functions pretty well. But it's [REDACTED], and doesn't like to roleplay.
Arch-Function-3B is fantastic at calling functions, and absolutely nothing else.
Dolphin3.0-Qwen2.5-3b is great at roleplay and refuses to refuse anything. But it sucks at calling functions.

So I created Ramius with MergeKit to try and get the best of both.
Plus I'm GPU poor and can't train. (Intel ARC cards come with buyer's remorse at no extra charge!)

The result is... mediocre. It correctly calls functions most of the time, but it tends to hallucinate function responses instead of calling the actual function.
But it does stay in character. YMMV.

The name comes from Marko Ramius, a fictional communist submarine commander who defects to the United States in Tom Clancy's The Hunt for Red October.
He's a former communist and the name sounded cool.

I've included the F16 and Q4_0 weights.

• Developed by: Other people's hard work.
• Funded by [optional]: Also other people's hard work.
• Shared by [optional]: Me.
• Model type: Autoregressive transformer.
• Language(s) (NLP): English, and others, probably.
• License: [More Information Needed]

Model Sources [optional]

Created with MergeKit.

models:
  - model: katanemo/Arch-Function-3B
    lambda: 1.0
    select_topk: 0.4
    weight: 0.7
  - model: cognitivecomputations/Dolphin3.0-Qwen2.5-3b
    density: 1.0
    lambda: 1.0
    select_topk: 0.6
    weight: 0.3
merge_method: sce
base_model: katanemo/Arch-Function-3B
parameters:
  int8_mask: true
  normalize: true
dtype: bfloat16

Bias, Risks, and Limitations

This is uncensored and does hallucinate. frequently.

[More Information Needed]

Recommendations

I use this with Ollama and Home Assistant via the Extended OpenAI conversation integration. Works best with a top P of around 0.95 and temperature around 0.85.

I also recommend you DO NOT put your entity states in your system prompt, and instead write functions to get the information. This will keep your system prompt static and more easily cached, which should reduce prompt processing time.