Quantization made by Richard Erkhov.

A-I-0xtom-7B-slerp - GGUF

Model creator: https://huggingface.co/InnerI/
Original model: https://huggingface.co/InnerI/A-I-0xtom-7B-slerp/

Name	Quant method	Size
A-I-0xtom-7B-slerp.Q2_K.gguf	Q2_K	2.53GB
A-I-0xtom-7B-slerp.IQ3_XS.gguf	IQ3_XS	2.81GB
A-I-0xtom-7B-slerp.IQ3_S.gguf	IQ3_S	2.96GB
A-I-0xtom-7B-slerp.Q3_K_S.gguf	Q3_K_S	2.95GB
A-I-0xtom-7B-slerp.IQ3_M.gguf	IQ3_M	3.06GB
A-I-0xtom-7B-slerp.Q3_K.gguf	Q3_K	3.28GB
A-I-0xtom-7B-slerp.Q3_K_M.gguf	Q3_K_M	3.28GB
A-I-0xtom-7B-slerp.Q3_K_L.gguf	Q3_K_L	3.56GB
A-I-0xtom-7B-slerp.IQ4_XS.gguf	IQ4_XS	3.67GB
A-I-0xtom-7B-slerp.Q4_0.gguf	Q4_0	3.83GB
A-I-0xtom-7B-slerp.IQ4_NL.gguf	IQ4_NL	3.87GB
A-I-0xtom-7B-slerp.Q4_K_S.gguf	Q4_K_S	3.86GB
A-I-0xtom-7B-slerp.Q4_K.gguf	Q4_K	4.07GB
A-I-0xtom-7B-slerp.Q4_K_M.gguf	Q4_K_M	4.07GB
A-I-0xtom-7B-slerp.Q4_1.gguf	Q4_1	4.24GB
A-I-0xtom-7B-slerp.Q5_0.gguf	Q5_0	4.65GB
A-I-0xtom-7B-slerp.Q5_K_S.gguf	Q5_K_S	4.65GB
A-I-0xtom-7B-slerp.Q5_K.gguf	Q5_K	4.78GB
A-I-0xtom-7B-slerp.Q5_K_M.gguf	Q5_K_M	4.78GB
A-I-0xtom-7B-slerp.Q5_1.gguf	Q5_1	5.07GB
A-I-0xtom-7B-slerp.Q6_K.gguf	Q6_K	5.53GB
A-I-0xtom-7B-slerp.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 tags: - merge - mergekit - lazymergekit - 0x0dad0/nous_nous_v2_0 - tomaszki/nous-thirty base_model: - 0x0dad0/nous_nous_v2_0 - tomaszki/nous-thirty model-index: - name: A-I-0xtom-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: 58.19 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InnerI/A-I-0xtom-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: 77.64 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InnerI/A-I-0xtom-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: 58.74 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InnerI/A-I-0xtom-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: 54.78 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InnerI/A-I-0xtom-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: 73.24 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InnerI/A-I-0xtom-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: 40.18 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=InnerI/A-I-0xtom-7B-slerp name: Open LLM Leaderboard

A-I-0xtom-7B-slerp

A-I-0xtom-7B-slerp is a merge of the following models using LazyMergekit:

Avg model loss 0.3912096044793725

I used this testing script that loads your local model, pulls the latest data from cortex and calculates the loss: avg loss script

🧩 Configuration

slices:
  - sources:
      - model: 0x0dad0/nous_nous_v2_0
        layer_range: [0, 32]
      - model: tomaszki/nous-thirty
        layer_range: [0, 32]
merge_method: slerp
base_model: 0x0dad0/nous_nous_v2_0
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: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "InnerI/A-I-0xtom-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	60.46
AI2 Reasoning Challenge (25-Shot)	58.19
HellaSwag (10-Shot)	77.64
MMLU (5-Shot)	58.74
TruthfulQA (0-shot)	54.78
Winogrande (5-shot)	73.24
GSM8k (5-shot)	40.18