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RichardErkhov/
Stopwolf_-_Tito-7B-slerp-gguf

Name: Stopwolf_-_Tito-7B-slerp-gguf
Availability: InStock

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Tito-7B-slerp - GGUF

Model creator: https://huggingface.co/Stopwolf/
Original model: https://huggingface.co/Stopwolf/Tito-7B-slerp/

Name	Quant method	Size
Tito-7B-slerp.Q2_K.gguf	Q2_K	2.53GB
Tito-7B-slerp.IQ3_XS.gguf	IQ3_XS	2.81GB
Tito-7B-slerp.IQ3_S.gguf	IQ3_S	2.96GB
Tito-7B-slerp.Q3_K_S.gguf	Q3_K_S	2.95GB
Tito-7B-slerp.IQ3_M.gguf	IQ3_M	3.06GB
Tito-7B-slerp.Q3_K.gguf	Q3_K	3.28GB
Tito-7B-slerp.Q3_K_M.gguf	Q3_K_M	3.28GB
Tito-7B-slerp.Q3_K_L.gguf	Q3_K_L	3.56GB
Tito-7B-slerp.IQ4_XS.gguf	IQ4_XS	3.67GB
Tito-7B-slerp.Q4_0.gguf	Q4_0	3.83GB
Tito-7B-slerp.IQ4_NL.gguf	IQ4_NL	3.87GB
Tito-7B-slerp.Q4_K_S.gguf	Q4_K_S	3.86GB
Tito-7B-slerp.Q4_K.gguf	Q4_K	4.07GB
Tito-7B-slerp.Q4_K_M.gguf	Q4_K_M	4.07GB
Tito-7B-slerp.Q4_1.gguf	Q4_1	4.24GB
Tito-7B-slerp.Q5_0.gguf	Q5_0	4.65GB
Tito-7B-slerp.Q5_K_S.gguf	Q5_K_S	4.65GB
Tito-7B-slerp.Q5_K.gguf	Q5_K	4.78GB
Tito-7B-slerp.Q5_K_M.gguf	Q5_K_M	4.78GB
Tito-7B-slerp.Q5_1.gguf	Q5_1	5.07GB
Tito-7B-slerp.Q6_K.gguf	Q6_K	5.53GB
Tito-7B-slerp.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 tags:

merge
mergekit
lazymergekit
gordicaleksa/YugoGPT
mlabonne/AlphaMonarch-7B model-index:
name: Tito-7B-slerp 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: 68.09 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp 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: 86.38 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp 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: 64.01 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp 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: 57.01 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp 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: 81.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp 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: 63.61 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Stopwolf/Tito-7B-slerp name: Open LLM Leaderboard

Tito-7B-slerp

Tito-7B-slerp is a merge of the following models using mergekit:

🧩 Configuration

yaml

slices:
  - sources:
      - model: gordicaleksa/YugoGPT
        layer_range: [0, 32]
      - model: mlabonne/AlphaMonarch-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/AlphaMonarch-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.6
dtype: bfloat16

Results

Evaluations on Serbian LLM eval suite (or rather, performance and knowledge of Serbian):

	ARC-E	ARC-C	Hellaswag	BoolQ	Winogrande	OpenbookQA	PiQA	NQ Open	TriviaQA	Avg.
Zamfir-7B	51.85	32.25	46.03	75.59	62.59	26.00	66.81	16.09	36.11	45.92
Mustra-7B	52.95	33.70	45.89	77.55	64.17	30.60	67.25	15.40	34.84	46.93
Tito-7B	55.43	34.73	48.19	77.37	65.27	30.00	67.30	16.7	35.38	47.82
YugoGPT	57.79	34.73	49.89	69.45	64.56	28.20	72.03	15.82	36.14	47.62

Here, all benchmarks were done 0-shot, on the exception of NQ Open and TriviaQA which were done in 5-shot manner, in order to be comparable to Mistral paper.

If we try to replicate OpenLLM Leaderboard results on available Serbian datasets (running an appropriate amount of shots instead of 0), we get:

	ARC	Hellaswag	Winogrande	TruthfulQA	Avg.
Tito-7B	47.27	-	69.93	57.48	58.23
Perucac-7B	49.74	-	71.98	56.03	59.25
YugoGPT	44.03	-	70.64	48.06	54.24
Llama3-8B	42.24	-	61.25	51.08	51.52
SambaLingo	37.88	-	61.48	47.23	48.86

Note that YugoGPT, Llama3 and SambaLingo are all base models, unlike Tito and Perucac.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Tito	YugoGPT
Avg.	70.13	57.34
AI2 Reasoning Challenge (25-Shot)	68.09	58.10
HellaSwag (10-Shot)	86.38	81.44
MMLU (5-Shot)	64.01	60.68
TruthfulQA (0-shot)	57.01	36.60
Winogrande (5-shot)	81.69	76.56
GSM8k (5-shot)	63.61	30.70

RichardErkhov/Stopwolf_-_Tito-7B-slerp-gguf