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!

https://www.linkedin.com/in/pankajam Looking forward to connecting!

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

python

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:

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

pip install "vllm>=0.4.3"

Or you can install vLLM from source.

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

json
1 { 2 "architectures": [ 3 "Qwen2ForCausalLM" 4 ], 5 // ... 6 "vocab_size": 152064, 7 // adding the following snippets 8 "rope_scaling": { 9 "factor": 4.0, 10 "original_max_position_embeddings": 32768, 11 "type": "yarn" 12 } 13 }

This snippet enable YARN to support longer contexts.
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:

bash
1curl http://localhost:8000/v1/chat/completions \ 2 -H "Content-Type: application/json" \ 3 -d '{ 4 "model": "pankajmathur/orca_mini_v7_7b", 5 "messages": [ 6 {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."}, 7 {"role": "user", "content": "Hello Orca Mini, what can you do for me?"} 8 ] 9 }'

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.

emplitude/rubywork

Qwen2 orca_mini_v7_7b is trained with various SFT Datasets

NOTICE

Evaluation

Example Usage

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

emplitude/
rubywork