README.md

metadata

license: apache-2.0
tags:
  - merge
  - mergekit
  - mistral
  - xDAN-AI/xDAN-L1-Chat-RL-v1
  - fhai50032/BeagleLake-7B-Toxic
base_model:
  - xDAN-AI/xDAN-L1-Chat-RL-v1
  - fhai50032/BeagleLake-7B-Toxic
model-index:
  - name: xLakeChat
    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: 62.37
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
          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: 82.64
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
          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: 59.32
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
          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: 52.96
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
          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: 74.74
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
          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: 50.27
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
          name: Open LLM Leaderboard

xLakeChat

xLakeChat is a merge of the following models

• xDAN-AI/xDAN-L1-Chat-RL-v1
• fhai50032/BeagleLake-7B-Toxic

🧩 Configuration

models:
  - model: senseable/WestLake-7B-v2
# no params for base model
  - model: xDAN-AI/xDAN-L1-Chat-RL-v1
    parameters:
      weight: 0.73
      density: 0.64
  - model: fhai50032/BeagleLake-7B-Toxic
    parameters:
      weight: 0.46
      density: 0.55
merge_method: dare_ties
base_model: senseable/WestLake-7B-v2
parameters:
  normalize: true
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "fhai50032/xLakeChat"
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.	63.72
AI2 Reasoning Challenge (25-Shot)	62.37
HellaSwag (10-Shot)	82.64
MMLU (5-Shot)	59.32
TruthfulQA (0-shot)	52.96
Winogrande (5-shot)	74.74
GSM8k (5-shot)	50.27