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RichardErkhov/
MozaicAI_-_Mozaic-7B-gguf

Name: MozaicAI_-_Mozaic-7B-gguf
Availability: InStock

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Mozaic-7B - GGUF

Model creator: https://huggingface.co/MozaicAI/
Original model: https://huggingface.co/MozaicAI/Mozaic-7B/

Name	Quant method	Size
Mozaic-7B.Q2_K.gguf	Q2_K	2.53GB
Mozaic-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
Mozaic-7B.Q3_K.gguf	Q3_K	3.28GB
Mozaic-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
Mozaic-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
Mozaic-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
Mozaic-7B.Q4_0.gguf	Q4_0	3.83GB
Mozaic-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
Mozaic-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
Mozaic-7B.Q4_K.gguf	Q4_K	4.07GB
Mozaic-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
Mozaic-7B.Q4_1.gguf	Q4_1	4.24GB
Mozaic-7B.Q5_0.gguf	Q5_0	4.65GB
Mozaic-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
Mozaic-7B.Q5_K.gguf	Q5_K	4.78GB
Mozaic-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
Mozaic-7B.Q5_1.gguf	Q5_1	5.07GB
Mozaic-7B.Q6_K.gguf	Q6_K	5.53GB
Mozaic-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 library_name: transformers datasets:

argilla/distilabel-intel-orca-dpo-pairs pipeline_tag: text-generation model-index:
name: Evangelion-7B 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: 68.94 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B 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: 86.45 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B 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: 63.97 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B 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: 64.01 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B 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: 79.95 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B 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: 66.94 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=VitalContribution/Evangelion-7B name: Open LLM Leaderboard

Mozaic-7B (prev. Evangelion-7B)

We were curious to see what happens if one uses: $$ \text{{high-quality DPO dataset}} + \text{{merge of DPO optimized and non-DPO optimized model}} $$

The underlying model that I used was /Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp.

Dataset

Dataset: /argilla/distilabel-intel-orca-dpo-pairs

The dataset was roughly ~3000 samples but they were high quality (according to the chosen_score).
The following filters were applied to the original dataset:

python

dataset = dataset.filter(
    lambda r:
        r["status"] != "tie" and
        r["chosen_score"] >= 8 and
        not r["in_gsm8k_train"]
)

Chat Template

I decided to go with the ChatML which is used for OpenHermes2.5 By the way I integreated the chat template into the models tokenizer.

<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	71.71
AI2 Reasoning Challenge (25-Shot)	68.94
HellaSwag (10-Shot)	86.45
MMLU (5-Shot)	63.97
TruthfulQA (0-shot)	64.01
Winogrande (5-shot)	79.95
GSM8k (5-shot)	66.94

RichardErkhov/MozaicAI_-_Mozaic-7B-gguf