Quantization made by Richard Erkhov.

BeagleLake-7B - GGUF

Model creator: https://huggingface.co/fhai50032/
Original model: https://huggingface.co/fhai50032/BeagleLake-7B/

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

Original model description:

license: apache-2.0 tags: - merge - mergekit - mistral - fhai50032/RolePlayLake-7B - mlabonne/NeuralBeagle14-7B base_model: - fhai50032/RolePlayLake-7B - mlabonne/NeuralBeagle14-7B model-index: - name: BeagleLake-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: 70.39 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-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: 87.38 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-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: 64.25 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-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: 64.92 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-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: 83.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-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: 63.91 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/BeagleLake-7B name: Open LLM Leaderboard

BeagleLake-7B

BeagleLake-7B is a merge of the following models :

Merging models are not powerful but are helpful in the case that it can work like Transfer Learning similar idk.. But they perform high on Leaderboard For ex. NeuralBeagle is powerful model with lot of potential to grow and RolePlayLake is Suitable for RP (No-Simping) and is significantly uncensored and nice obligations Fine-tuning a Merged model as a base model is surely a way to look forward and see a lot of potential going forward..

Much thanks to Charles Goddard for making simple interface 'mergekit'

🧩 Configuration

models:
  - model: mlabonne/NeuralBeagle14-7B
# no params for base model
  - model: fhai50032/RolePlayLake-7B
    parameters:
      weight: 0.8
      density: 0.6
  - model: mlabonne/NeuralBeagle14-7B
    parameters:
      weight: 0.3
      density: [0.1,0.3,0.5,0.7,1]
merge_method: dare_ties
base_model: mlabonne/NeuralBeagle14-7B
parameters:
  normalize: true
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "fhai50032/BeagleLake-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.	72.34
AI2 Reasoning Challenge (25-Shot)	70.39
HellaSwag (10-Shot)	87.38
MMLU (5-Shot)	64.25
TruthfulQA (0-shot)	64.92
Winogrande (5-shot)	83.19
GSM8k (5-shot)	63.91