license, model-index

license

model-index

apache-2.0

name results open-llama-3b-v2-chat task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 40.61 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 70.3 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 28.73 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 37.84 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 65.51 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 2.58 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat Open LLM Leaderboard

Prerequisites

In addition to pytorch and transformers, install required packages:

pip install sentencepiece

Usage

To use, copy the following script:

ffrom transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'mediocredev/open-llama-3b-v2-chat'
tokenizer_id = 'mediocredev/open-llama-3b-v2-chat'
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

chat_history = [
    {"role": "user", "content": "Hello!"},
    {"role": "assistant", "content": "I am here."},
    {"role": "user", "content": "How many days are there in a leap year?"},
]

input_ids = tokenizer.apply_chat_template(
    chat_history, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
output_tokens = model.generate(
    input_ids,
    repetition_penalty=1.05,
    max_new_tokens=1000,
)
output_text = tokenizer.decode(
    output_tokens[0][len(input_ids[0]) :], skip_special_tokens=True
)

print(output_text)
# Assistant: There are 366 days in a leap year, which is one more day than the standard year.

Limitations

mediocredev/open-llama-3b-v2-chat is based on LLaMA 3B v2. It can struggle with factual accuracy, particularly when presented with conflicting information or nuanced topics. Its outputs are not deterministic and require critical evaluation to avoid relying solely on its assertions. Additionally, its generative capabilities, while promising, can sometimes produce factually incorrect or offensive content, necessitating careful curation and human oversight. As an evolving model, LLaMA is still under development, and its limitations in areas like bias mitigation and interpretability are being actively addressed. By using this model responsibly and being aware of its shortcomings, we can unlock its potential while mitigating its risks.

Contact

Welcome any feedback, questions, and discussions. Feel free to reach out: mediocredev@outlook.com

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	40.93
AI2 Reasoning Challenge (25-Shot)	40.61
HellaSwag (10-Shot)	70.30
MMLU (5-Shot)	28.73
TruthfulQA (0-shot)	37.84
Winogrande (5-shot)	65.51
GSM8k (5-shot)	2.58