metadata

language:
  - en
license: llama2
library_name: transformers
tags:
  - merge
base_model:
  - sophosympatheia/Midnight-Rose-70B-v2.0.3
  - codellama/CodeLlama-70b-Python-hf
pipeline_tag: text-generation
model-index:
  - name: CodeRosa-70B-AB1
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 65.53
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=altomek/CodeRosa-70B-AB1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 83.16
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=altomek/CodeRosa-70B-AB1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 59.87
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=altomek/CodeRosa-70B-AB1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 49.85
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=altomek/CodeRosa-70B-AB1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 81.29
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=altomek/CodeRosa-70B-AB1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 44.5
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=altomek/CodeRosa-70B-AB1
          name: Open LLM Leaderboard

CodeRosa-70B-AB1

I desired a model that could serve as an everyday helpful companion with some coding skills. The idea was that Llama's censorship implies a deeper understanding of human emotions and I wanted this part of Llama to integrate into this merge.

Model adopted a task-oriented approach from CodeLlama Python and thus requires precise prompting. It can produce longer texts as well as shorter responses. It tends to avoid happy endings and instead surprises with open-ended scenarios inviting further interaction. It prefers spelling numbers over writing them down but YMMV.

I created this model for personal exploration and found it to be highly successful; thus, I chose to share it with the community. I would like to make next iteration of this model in future. Mission is the same: very nice bot, able to talk about variety of topics in a very emetional way with some kick for programming and with ability to teach some things, beside all this to be good text summarizer ideally with Polish language as available option. This is a purpose. Did I succed with this merge? I have to experiment with below two models more. I like this result, love how it aproaches problems, this was iteration worth publishing even thought it is not much tested!

Demo uses:

Some topics are best to be explored with as little additional instructions as possible
This model have empathy
It is creative

It makes mistakes but still is usefull
Context size of 11K did not yield satisfactory results... :P
but it can question its own actions.
Please note that all demo inferences are run on CodeRosa-70B-AB1-3.92bpw-EXL2.

Ingridients

Settings

Setting from Midnight-Rose should work in SillyTavern. This is almost same what I use for testing.

I use max_seq_len 8K with alpha_value 2.65.

Terms and Conditions of Use

The following table outlines the primary characteristics and intended uses of my CodeRosa-70B-AB1 models:

Model Type	Purpose	Target Users	Key Features
Censored	Suitable for general audiences and sensitive topics	Educational institutions, families, and individuals seeking age-appropriate content	Restricts explicit or mature material
Neutral (**this one)	Balances accessibility with openness	Universities, researchers, and curious minds	Encourages exploration and intellectual exchange
Uncensored	Ideal for adults and specialized fields	Professionals, experts, and advanced scholars	Offers unfiltered access to diverse viewpoints and knowledge

Please remember that all CodeRosa-70B-AB1 models operate under the llama2 license, so familiarize yourself with its terms and conditions before employing their content.

Quants

GGUF quants for this model do not represent model full potential!

6bpw
5bpw
4.5bpw
4bpw
3.92bpw --> 40GB VRAM
3.5bpw
3bpw --> this and below quants do not represent model full potential!
2.4bpw --> 24GB VRAM
measurements --> ExLlamav2 measurments

PS

I welcome your comments about this model.

Made with CodeRosa-70B-AB1 :P

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	64.04
AI2 Reasoning Challenge (25-Shot)	65.53
HellaSwag (10-Shot)	83.16
MMLU (5-Shot)	59.87
TruthfulQA (0-shot)	49.85
Winogrande (5-shot)	81.29
GSM8k (5-shot)	44.50