---
license: cc-by-nc-4.0
model-index:
- name: cr-model
  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: 57.85
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TwT-6/cr-model
      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: 81.66
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TwT-6/cr-model
      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: 68.73
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TwT-6/cr-model
      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: 58.2
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TwT-6/cr-model
      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: 76.24
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TwT-6/cr-model
      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: 65.88
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TwT-6/cr-model
      name: Open LLM Leaderboard
---

My model is a state-of-the-art language processing AI designed to understand and generate human-like text. It leverages deep learning algorithms to engage in a wide range of language tasks, providing users with information, recommendations, and even casual conversation. With a broad knowledge base and nuanced understanding of context, my capabilities enable me to assist with various inquiries and perform complex language-based tasks effectively.

How to use？

from transformers import AutoModelForCausalLM, AutoTokenizer

from transformers.generation import GenerationConfig

import torch


model = AutoModelForCausalLM.from_pretrained(
    'TwT-6/cr-model', 
    attn_implementation="flash_attention_2", 
    trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto").eval()
    
tokenizer = AutoTokenizer.from_pretrained('TwT-6/cr-model', trust_remote_code=True)

inputs = '你好'

inputs = f'<|omni_start|>### User:\n{inputs}\n\n### Assistant:\n'

inputs = tokenizer(inputs, return_tensors="pt").to('cuda')

output_ids = model.generate(**inputs)[0].cpu()

output = tokenizer.decode(output_ids[inputs.input_ids.shape[-1]:])

print(output)

## 你好！很高兴见到你。有什么我可以帮助你的吗

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TwT-6__cr-model)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |68.09|
|AI2 Reasoning Challenge (25-Shot)|57.85|
|HellaSwag (10-Shot)              |81.66|
|MMLU (5-Shot)                    |68.73|
|TruthfulQA (0-shot)              |58.20|
|Winogrande (5-shot)              |76.24|
|GSM8k (5-shot)                   |65.88|