Quantization made by Richard Erkhov.

KangalKhan-RawEmerald-7B - GGUF

Model creator: https://huggingface.co/Yuma42/
Original model: https://huggingface.co/Yuma42/KangalKhan-RawEmerald-7B/

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

Original model description:

language: - en license: apache-2.0 tags: - merge - mergekit - lazymergekit - argilla/CapybaraHermes-2.5-Mistral-7B - argilla/distilabeled-OpenHermes-2.5-Mistral-7B base_model: - argilla/CapybaraHermes-2.5-Mistral-7B - argilla/distilabeled-OpenHermes-2.5-Mistral-7B model-index: - name: KangalKhan-RawEmerald-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: 66.89 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-RawEmerald-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: 85.75 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-RawEmerald-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: 63.23 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-RawEmerald-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: 57.58 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-RawEmerald-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: 78.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-RawEmerald-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: 62.85 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Yuma42/KangalKhan-RawEmerald-7B name: Open LLM Leaderboard

KangalKhan-RawEmerald-7B

I suggest using ChatML (Use whatever system prompt you like, this is just an example!):

<|im_start|>system
You are a friendly assistant.<|im_end|>
<|im_start|>user
Hello, what are you?<|im_end|>
<|im_start|>assistant
I am an AI language model designed to assist users with information and answer their questions. How can I help you today?<|im_end|>

Q4_K_S GGUF:
https://huggingface.co/Yuma42/KangalKhan-RawEmerald-7B-GGUF

More GGUF variants by mradermacher:
WARNING: I have observed that these versions output typos in rare cases. If you have the same problem, use my Q4_K_S GGUF above. https://huggingface.co/mradermacher/KangalKhan-RawEmerald-7B-GGUF

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

🧩 Configuration

models:
  - model: teknium/OpenHermes-2.5-Mistral-7B
    # no parameters necessary for base model
  - model: argilla/CapybaraHermes-2.5-Mistral-7B
    parameters:
      density: 0.6
      weight: 0.5
  - model: argilla/distilabeled-OpenHermes-2.5-Mistral-7B
    parameters:
      density: 0.6
      weight: 0.5
merge_method: ties
base_model: teknium/OpenHermes-2.5-Mistral-7B
parameters:
  normalize: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Yuma42/KangalKhan-RawEmerald-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

Metric	Value
Avg.	69.09
AI2 Reasoning Challenge (25-Shot)	66.89
HellaSwag (10-Shot)	85.75
MMLU (5-Shot)	63.23
TruthfulQA (0-shot)	57.58
Winogrande (5-shot)	78.22
GSM8k (5-shot)	62.85