commit 077b36f76e9abc38a5f0e7340e22fc9e320c43b3 Author: ModelHub XC Date: Sun Aug 30 11:00:19 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: pankajmathur/orca_alpaca_3b Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx 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-0,0 +1,147 @@ +--- +language: +- en +library_name: transformers +datasets: +- psmathur/alpaca_orca +license: cc-by-nc-sa-4.0 +--- +# Orca_alpaca_3b +An Open_LLaMA-3B model trained on explain tuned datasets, created using Instructions and Input from Alpaca datasets and applying Orca Research Paper dataset construction approaches. + + +# Dataset + +We build explain tuned [Alpaca dataset ~52K](https://crfm.stanford.edu/2023/03/13/alpaca.html) created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). + +We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets. + +This helps student model aka this model to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version). + +Please see below example usage how the **System** prompt is added before each **instruction**. + +# Training + +The training configurations are provided in the table below. + +The training takes on 4x A600(50G) GPUs and lasts for around 20 Hours for cost of $66 using [Lambda Labs](https://lambdalabs.com) + +We used DeepSpeed with Zero-3 approaches for parallel gpu training by writing our own fine tunning scripts plus leveraging some of the model training code provided by amazing [OpenAlpaca repo](https://github.com/yxuansu/OpenAlpaca) + +Here are some of params used during training: + +||| +|:-------------:|:-------------:| +|*batch_size*|16| +|*train_micro_batch_size_per_gpu*|2| +|*gradient_accumulation_steps*|2| +|*Learning rate*|2e-5| +|*Max length*|1024| +|*Epochs*|3| + + + +# Example Usage + +Below shows an example on how to use [alpaca_orca_open_llama_3b](psmathur/alpaca_orca_open_llama_3b) + +```python +import torch +from transformers import LlamaForCausalLM, LlamaTokenizer + +# change model_path between 3b,7b or 13b +model_path = 'psmathur/alpaca_orca_open_llama_3b' +tokenizer = LlamaTokenizer.from_pretrained(model_path) +model = LlamaForCausalLM.from_pretrained( + model_path, torch_dtype=torch.float16, device_map='auto', +) + + +#generate text function +def generate_text(system, instruction, input=None): + + if input: + prompt = f"### System:\n{system}\n\n#\n\n### User:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n" + else: + prompt = f"### System:\n{system}\n\n#\n\n### User:\n{instruction}\n\n### Response:\n" + + tokens = tokenizer.encode(prompt) + tokens = torch.LongTensor(tokens).unsqueeze(0) + tokens = tokens.to('cuda') + + instance = {'input_ids': tokens,'top_p': 1.0, 'temperature':0.7, 'generate_len': 1024} + + length = len(tokens[0]) + with torch.no_grad(): + rest = model.generate( + input_ids=tokens, + max_length=length+instance['generate_len'], + use_cache=True, + do_sample=True, + top_p=instance['top_p'], + temperature=instance['temperature'] + ) + output = rest[0][length:] + string = tokenizer.decode(output, skip_special_tokens=True) + print(f'[!] Response: {string}') + +# same prompt as provided by Orca Research Paper +system = 'You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.' +instruction = 'Use the given data to calculate the median.' +input = '[5,2,3,4,1]' +generate_text(system, instruction, input) + +``` + +**P.S. I am #opentowork and #collaboration, if you can help, please reach out to me at www.linkedin.com/in/pankajam** + +Next Goals: +1) Try more data, Dolly V2, WizardLM, & Others (we are open for suggestions) +2) Try bigger OpenLLaMA models 7B and 13B +3) Try better GPU for training, couldn't get 8xA100 (40GB), I guess they are in hot demand now. +4) Provide more options for Text generation UI. (may be https://github.com/oobabooga/text-generation-webui) +6) Provide 4bit GGML/GPTQ quantized model (may be [TheBloke](https://huggingface.co/TheBloke) can help here) + + +Reference: +If you found [alpaca_orca_open_llama_3b](psmathur/alpaca_orca_open_llama_3b) useful in your research or applications, please kindly cite using the following BibTeX: + +``` +@misc{alpaca_orca_open_llama_3b, + author = {Pankaj Mathur}, + title = {alpaca_orca_open_llama_3b: A custom explain tuned Alpaca Model Based On OpenLLaMA}, + year = {2023}, + publisher = {GitHub, HuggingFace}, + journal = {GitHub repository, HuggingFace repository}, + howpublished = {\url{https://github.com/pankajarm/alpaca_orca_open_llama_3b}, \url{https://https://huggingface.co/psmathur/alpaca_orca_open_llama_3b}}, +} +``` +``` +@software{openlm2023openllama, + author = {Xinyang Geng and Hao Liu}, + title = {OpenLLaMA: An Open Reproduction of LLaMA}, + month = May, + year = 2023, + url = {https://github.com/openlm-research/open_llama} +} +``` +``` +@misc{openalpaca, + author = {Yixuan Su and Tian Lan and Deng Cai}, + title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA}, + year = {2023}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}}, +} +``` +``` +@misc{alpaca, + author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto }, + title = {Stanford Alpaca: An Instruction-following LLaMA model}, + year = {2023}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}}, +} +``` \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..39b6751 --- /dev/null +++ b/config.json @@ -0,0 +1,23 @@ +{ + "_name_or_path": "openlm-research/open_llama_3b", + "architectures": [ + "LlamaForCausalLM" + ], + "bos_token_id": 1, + "eos_token_id": 2, + "hidden_act": "silu", + "hidden_size": 3200, + "initializer_range": 0.02, + "intermediate_size": 8640, + "max_position_embeddings": 2048, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 26, + "pad_token_id": 0, + "rms_norm_eps": 1e-06, + "tie_word_embeddings": false, + "torch_dtype": "float32", + "transformers_version": "4.29.1", + "use_cache": true, + "vocab_size": 32000 +} diff --git a/generation_config.json 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