vilm_-_Quyen-Plus-v0.1-4bits

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Quyen-Plus-v0.1 - bnb 4bits

• Model creator: https://huggingface.co/vilm/
• Original model: https://huggingface.co/vilm/Quyen-Plus-v0.1/

Original model description:

language:

• en license: other library_name: transformers datasets:
• teknium/OpenHermes-2.5
• LDJnr/Capybara
• Intel/orca_dpo_pairs
• argilla/distilabel-capybara-dpo-7k-binarized pipeline_tag: text-generation model-index:
• name: Quyen-Plus-v0.1 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: 55.72 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1 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: 78.52 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1 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: 60.45 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1 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: 53.6 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1 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: 71.27 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1 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: 60.05 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vilm/Quyen-Plus-v0.1 name: Open LLM Leaderboard

---

Quyen

<img src="quyen.webp" width="512" height="512" alt="Quyen">

Model Description

Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:

• Quyen-SE (0.5B)
• Quyen-Mini (1.8B)
• Quyen (4B)
• Quyen-Plus (7B)
• Quyen-Pro (14B)
• Quyen-Pro-Max (72B)

All models were trained with SFT and DPO using the following dataset:

• OpenHermes-2.5 by Teknium
• Capyabara by LDJ
• argilla/distilabel-capybara-dpo-7k-binarized by argilla
• orca_dpo_pairs by Intel
• and Private Data by Ontocord & BEE-spoke-data

Prompt Template

• All Quyen models use ChatML as the default template:

<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Hello world.<|im_end|>
<|im_start|>assistant

• You can also use apply_chat_template:

messages = [
    {"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."},
    {"role": "user", "content": "Hello world."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)

Benchmarks:

• Coming Soon! We will update the benchmarks later

Acknowledgement

• We're incredibly grateful to Tensoic and Ontocord for their generous support with compute and data preparation.
• Special thanks to the Qwen team for letting us access the models early for these amazing finetunes.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here