pankajmathur_-_orca_mini_v7_7b-gguf

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orca_mini_v7_7b - GGUF

• Model creator: https://huggingface.co/pankajmathur/
• Original model: https://huggingface.co/pankajmathur/orca_mini_v7_7b/

Original model description:

license: apache-2.0 model-index:

• name: orca_mini_v7_7b results:
  • task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics:
    • type: inst_level_strict_acc and prompt_level_strict_acc value: 43.88 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v7_7b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics:
    • type: acc_norm value: 33.95 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v7_7b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics:
    • type: exact_match value: 2.64 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v7_7b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics:
    • type: acc_norm value: 6.15 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v7_7b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics:
    • type: acc_norm value: 12.66 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v7_7b name: Open LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics:
    • type: acc value: 35.19 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=pankajmathur/orca_mini_v7_7b name: Open LLM Leaderboard

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Model Name: Qwen2 orca_mini_v7_7b

Qwen2 orca_mini_v7_7b is trained with various SFT Datasets

<img src="https://huggingface.co/pankajmathur/orca_mini_v5_8b/resolve/main/orca_minis_small.jpeg" width="auto" /> <strong> Passionate about Generative AI? I help companies to privately train and deploy custom LLM/MLLM affordably. For startups, I can even assist with securing GPU grants to get you started. Let's chat!

<a href="https://www.linkedin.com/in/pankajam" target="_blank">https://www.linkedin.com/in/pankajam</a> Looking forward to connecting! </strong>

<br>

NOTICE

By providing proper credit and attribution, you are granted permission to use this model as a foundational base for further Full fine tuning, DPO, PPO or ORPO tuning and any kind of Merges. I actively encourage users to customize and enhance the model according to their specific needs, as this version is designed to be a comprehensive general model. Dive in and innovate!

Evaluation

Coming Soon..

Example Usage

Here is the ChatML prompt format

<|im_start|>system
You are Orca Mini, a helpful AI assistant.<|im_end|>
<|im_start|>user
Hello Orca Mini, what can you do for me?<|im_end|>
<|im_start|>assistant

Below shows a code example on how to use this model

from transformers import AutoModel, AutoTokenizer
model_slug = "pankajmathur/orca_mini_v7_7b"
model = AutoModel.from_pretrained(model_slug)
tokenizer = AutoTokenizer.from_pretrained(model_slug)
messages = [
    {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
    {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)

Quants

GGUF : Coming Soon

AWQ: Coming Soon

Processing Long Texts (Based upon Qwen2-7B-Instruct suggestions at https://huggingface.co/Qwen/Qwen2-7B-Instruct)

To handle extensive inputs exceeding 32,768 tokens, we utilize YARN, a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.

For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps:

1. Install vLLM: You can install vLLM by running the following command.

pip install "vllm>=0.4.3"

Or you can install vLLM from source.

1. Configure Model Settings: After downloading the model weights, modify the config.json file by including the below snippet:

       {
           "architectures": [
               "Qwen2ForCausalLM"
           ],
           // ...
           "vocab_size": 152064,
           // adding the following snippets
           "rope_scaling": {
               "factor": 4.0,
               "original_max_position_embeddings": 32768,
               "type": "yarn"
           }
       }
   

   This snippet enable YARN to support longer contexts.

2. Model Deployment: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command:

   python -u -m vllm.entrypoints.openai.api_server --model pankajmathur/orca_mini_v7_7b
   

   Then you can access the Chat API by:

   curl http://localhost:8000/v1/chat/completions \
       -H "Content-Type: application/json" \
       -d '{
       "model": "pankajmathur/orca_mini_v7_7b",
       "messages": [
         {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
         {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
       ]
       }'
   

Note: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the rope_scaling configuration only when processing long contexts is required.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here