mediocredev_-_open-llama-3b-v2-chat-gguf

Quantization made by Richard Erkhov.

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open-llama-3b-v2-chat - GGUF

• Model creator: https://huggingface.co/mediocredev/
• Original model: https://huggingface.co/mediocredev/open-llama-3b-v2-chat/

Original model description:

license: apache-2.0 model-index:

• name: open-llama-3b-v2-chat 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: 40.61 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat 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: 70.3 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat 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: 28.73 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat 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: 37.84 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat 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: 65.51 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat 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: 2.58 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mediocredev/open-llama-3b-v2-chat name: Open LLM Leaderboard

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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: [email protected]

Open LLM Leaderboard Evaluation Results

Detailed results can be found here