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RichardErkhov/Yuma42_-_KangalKhan-ShinyEmerald-7B-gguf

sourceHugging Faceupdated 2y agoView on Hugging Face
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Quantization made by Richard Erkhov.

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KangalKhan-ShinyEmerald-7B - GGUF

  • —Model creator: https://huggingface.co/Yuma42/
  • —Original model: https://huggingface.co/Yuma42/KangalKhan-ShinyEmerald-7B/

Original model description: --- language:

  • —en license: apache-2.0 tags:
  • —merge
  • —mergekit
  • —lazymergekit
  • —Yuma42/KangalKhan-Sapphire-7B
  • —Yuma42/KangalKhan-Ruby-7B-Fixed base_model:
  • —Yuma42/KangalKhan-Sapphire-7B
  • —Yuma42/KangalKhan-Ruby-7B-Fixed model-index:
  • —name: KangalKhan-ShinyEmerald-7B results:
  • —task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2arc config: ARC-Challenge split: test args: numfew_shot: 25 metrics:
  • —type: accnorm value: 66.21 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllm_leaderboard?query=Yuma42/KangalKhan-ShinyEmerald-7B name: Open LLM Leaderboard
  • —task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: numfewshot: 10 metrics:
  • —type: accnorm value: 85.37 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllm_leaderboard?query=Yuma42/KangalKhan-ShinyEmerald-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: numfewshot: 5 metrics:
  • —type: acc value: 63.36 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=Yuma42/KangalKhan-ShinyEmerald-7B name: Open LLM Leaderboard
  • —task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthfulqa config: multiplechoice split: validation args: numfewshot: 0 metrics:
  • —type: mc2 value: 56.65 source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=Yuma42/KangalKhan-ShinyEmerald-7B name: Open LLM Leaderboard
  • —task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winograndexl split: validation args: numfew_shot: 5 metrics:
  • —type: acc value: 78.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=Yuma42/KangalKhan-ShinyEmerald-7B name: Open LLM Leaderboard
  • —task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: numfewshot: 5 metrics:
  • —type: acc value: 61.79 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=Yuma42/KangalKhan-ShinyEmerald-7B name: Open LLM Leaderboard ---

KangalKhan-ShinyEmerald-7B

KangalKhan-ShinyEmerald-7B is a merge of the following models using LazyMergekit:

🧩 Configuration

yaml
models:
  - model: teknium/OpenHermes-2.5-Mistral-7B
    # no parameters necessary for base model
  - model: Yuma42/KangalKhan-Sapphire-7B
    parameters:
      density: 0.6
      weight: 0.5
  - model: Yuma42/KangalKhan-Ruby-7B-Fixed
    parameters:
      density: 0.6
      weight: 0.5
merge_method: ties
base_model: teknium/OpenHermes-2.5-Mistral-7B
parameters:
  normalize: true
dtype: bfloat16

💻 Usage

python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Yuma42/KangalKhan-ShinyEmerald-7B"
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

MetricValue
Avg.68.63
AI2 Reasoning Challenge (25-Shot)66.21
HellaSwag (10-Shot)85.37
MMLU (5-Shot)63.36
TruthfulQA (0-shot)56.65
Winogrande (5-shot)78.37
GSM8k (5-shot)61.79