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--- |
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language: |
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- en |
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license: other |
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library_name: transformers |
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datasets: |
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- psmathur/orca_mini_v1_dataset |
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- ehartford/dolphin |
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pipeline_tag: text-generation |
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model-index: |
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- name: orca_mini_v3_7b |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 56.91 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/orca_mini_v3_7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 79.64 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/orca_mini_v3_7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 52.37 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/orca_mini_v3_7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 50.51 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/orca_mini_v3_7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 74.27 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/orca_mini_v3_7b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 7.13 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=psmathur/orca_mini_v3_7b |
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name: Open LLM Leaderboard |
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--- |
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# orca_mini_v3_7b |
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A LLama2-7b model trained on Orca Style datasets. |
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<br> |
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![orca-mini](https://huggingface.co/psmathur/orca_mini_v3_7b/resolve/main/orca_minis_small.jpeg) |
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<br> |
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🤔 How good is orca-mini-v3-7b? Do the evaluation results from HuggingFace Open LLM leaderboard translate to real-world use cases? |
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🔍 Now you can figure it out for yourself! |
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Introducing the orca-mini chatbot powered by the orca-mini-v3-7b model. Dive in and see how the open source 7b model stacks up in the world of massive language models. 🌍 |
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⏰ Hurry up before I run out of GPU credits! 😉 |
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Check it out here 👉 |
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[https://huggingface.co/spaces/psmathur/psmathur-orca_mini_v3_7b](https://huggingface.co/spaces/psmathur/psmathur-orca_mini_v3_7b) |
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<br> |
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**P.S. If you're interested to collaborate, please connect with me at www.linkedin.com/in/pankajam.** |
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<br> |
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### quantized versions |
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Big thanks to [@TheBloke](https://huggingface.co/TheBloke) |
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1) https://huggingface.co/TheBloke/orca_mini_v3_7B-GGML |
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2) https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ |
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<br> |
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#### license disclaimer: |
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This model is bound by the license & usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind. |
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<br> |
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## evaluation |
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We evaluated orca_mini_v3_7b on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI. |
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Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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||||| |
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|:------:|:--------:|:-------:|:--------:| |
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|**Task**|**Metric**|**Value**|**Stderr**| |
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|*arc_challenge*|acc_norm|0.5717|0.0145| |
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|*hellaswag*|acc_norm|0.7966|0.0043| |
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|*mmlu*|acc_norm|0.5234|0.035| |
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|*truthfulqa_mc*|mc2|0.5029|0.0156| |
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|**Total Average**|-|**0.59865**|| |
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<br> |
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## example esage |
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Here is prompt format |
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``` |
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### System: |
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You are an AI assistant that follows instruction extremely well. Help as much as you can. |
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### User: |
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Tell me about Orcas. |
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### Assistant: |
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``` |
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Below shows a code example on how to use this model |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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tokenizer = AutoTokenizer.from_pretrained("psmathur/orca_mini_v3_7b", use_fast=False) |
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model = AutoModelForCausalLM.from_pretrained( |
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"psmathur/orca_mini_v3_7b", |
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torch_dtype=torch.float16, |
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load_in_8bit=True, |
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low_cpu_mem_usage=True, |
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device_map="auto" |
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) |
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system_prompt = "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n" |
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#generate text steps |
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instruction = "Tell me about Orcas." |
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prompt = f"{system_prompt}### User: {instruction}\n\n### Assistant:\n" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=4096) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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``` |
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<br> |
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#### limitations & biases: |
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While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. |
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Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. |
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Exercise caution and cross-check information when necessary. |
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<br> |
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### citiation: |
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Please kindly cite using the following BibTeX: |
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``` |
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@misc{orca_mini_v3_7b, |
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author = {Pankaj Mathur}, |
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title = {orca_mini_v3_7b: An explain tuned Llama2-7b model}, |
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year = {2023}, |
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publisher = {GitHub, HuggingFace}, |
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journal = {GitHub repository, HuggingFace repository}, |
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howpublished = {\url{https://https://huggingface.co/psmathur/orca_mini_v3_7b}, |
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} |
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``` |
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|
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``` |
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@misc{mukherjee2023orca, |
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title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4}, |
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author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah}, |
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year={2023}, |
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eprint={2306.02707}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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|
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``` |
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@software{touvron2023llama, |
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title={LLaMA2: Open and Efficient Foundation Language Models}, |
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author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume}, |
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journal={arXiv preprint arXiv:2302.13971}, |
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year={2023} |
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} |
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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__orca_mini_v3_7b) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 47.98 | |
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| ARC (25-shot) | 56.91 | |
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| HellaSwag (10-shot) | 79.64 | |
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| MMLU (5-shot) | 52.37 | |
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| TruthfulQA (0-shot) | 50.51 | |
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| Winogrande (5-shot) | 74.27 | |
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| GSM8K (5-shot) | 7.13 | |
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| DROP (3-shot) | 15.06 | |
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|
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__orca_mini_v3_7b) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |53.47| |
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|AI2 Reasoning Challenge (25-Shot)|56.91| |
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|HellaSwag (10-Shot) |79.64| |
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|MMLU (5-Shot) |52.37| |
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|TruthfulQA (0-shot) |50.51| |
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|Winogrande (5-shot) |74.27| |
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|GSM8k (5-shot) | 7.13| |
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