fhai50032_-_BeagleLake-7B-8bits

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BeagleLake-7B - bnb 8bits

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

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

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BeagleLake-7B

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

• fhai50032/RolePlayLake-7B
• mlabonne/NeuralBeagle14-7B

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