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Model: pankajmathur/orca_alpaca_3b Source: Original Platform
2026-08-30 11:00:19 +08:00
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+---
+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}},
+}
+```