From 59f2d1a9e32b80e39e30aed1e2c36fce050c5eb8 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Wed, 29 Apr 2026 09:51:04 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: VMware/open-llama-7b-open-instruct Source: Original Platform --- .gitattributes | 34 ++++ README.md | 91 +++++++++ config.json | 23 +++ generation_config.json | 7 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 330 +++++++++++++++++++++++++++++++ pytorch_model-00001-of-00002.bin | 3 + pytorch_model-00002-of-00002.bin | 3 + pytorch_model.bin.index.json | 330 +++++++++++++++++++++++++++++++ special_tokens_map.json | 24 +++ tokenizer.model | 3 + tokenizer_config.json | 34 ++++ 13 files changed, 888 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 pytorch_model-00001-of-00002.bin create mode 100644 pytorch_model-00002-of-00002.bin create mode 100644 pytorch_model.bin.index.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.model create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..c7d9f33 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,34 @@ +*.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 filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..e632503 --- /dev/null +++ b/README.md @@ -0,0 +1,91 @@ +--- +license: cc-by-sa-3.0 +datasets: +- VMware/open-instruct-v1-oasst-dolly-hhrlhf +language: +- en +library_name: transformers +pipeline_tag: text-generation +--- + +# VMware/open-llama-7B-open-instruct +Instruction-tuned version of the fully trained Open LLama 7B model. The model is open for COMMERCIAL USE.
+ + There is a v2 version of this model available, https://huggingface.co/VMware/open-llama-7b-v2-open-instruct + + NOTE : The model was trained using the Alpaca prompt template + NOTE : Fast tokenizer results in incorrect encoding, set the ```use_fast = False``` parameter, when instantiating the tokenizer + +## License +- Commercially Viable +- Instruction dataset, [VMware/open-instruct-v1-oasst-dolly-hhrlhf](https://huggingface.co/datasets/VMware/open-instruct-v1-oasst-dolly-hhrlhf) is under cc-by-sa-3.0 +- Language Model, ([openlm-research/open_llama_7b](https://huggingface.co/openlm-research/open_llama_7b)) is under apache-2.0 + + +## Nomenclature + +- Model : Open-llama +- Model Size: 7B parameters +- Dataset: Open-instruct-v1 (oasst,dolly, hhrlhf) + +## Use in Transformers + +``` +import os +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = 'VMware/open-llama-7b-open-instruct' + + +tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False) + +model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map='sequential') + +prompt_template = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" + +prompt = 'Explain in simple terms how the attention mechanism of a transformer model works' + + +inputt = prompt_template.format(instruction= prompt) +input_ids = tokenizer(inputt, return_tensors="pt").input_ids.to("cuda") + +output1 = model.generate(input_ids, max_length=512) +input_length = input_ids.shape[1] +output1 = output1[:, input_length:] +output = tokenizer.decode(output1[0]) + +print(output) + +''' + Attention is a mechanism used in deep learning models, such as transformer models, to capture global dependencies between different parts of the input. In a transformer model, the attention mechanism works by computing a weighted sum of the input vectors and then applying a non-linear activation function to the result. + +The attention mechanism in a transformer model works in two steps: + +1. Query-Key Mapping: First, the input sequence is divided into two parts: the query vector and the key vector. The query vector represents the input at the current position, and the key vector represents the input at a previous position. + +2. Attention Weight Calculation: Second, the attention weights are calculated using the dot product between the query vector and each key vector. The attention weights represent the importance of the input at the previous position to the current position. + +The attention weights are then used to compute the attention score for each input element. The attention score represents the relevance of the input element to the current position. + +The attention mechanism in a transformer model is designed to capture global dependencies between different parts of the input. By attending to input elements from different positions, the model can learn to understand the relationships between different parts of the input. This allows the model to perform more complex tasks, such as understanding the relationships between words in a sentence or pixels in an image. + +''' +``` + +## Finetuning details +The finetuning scripts will be available in our [RAIL Github Repository](https://github.com/vmware-labs/research-and-development-artificial-intelligence-lab/tree/main/instruction-tuning) + +# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) +Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_VMware__open-llama-7b-open-instruct) + +| Metric | Value | +|-----------------------|---------------------------| +| Avg. | 40.9 | +| ARC (25-shot) | 49.74 | +| HellaSwag (10-shot) | 73.67 | +| MMLU (5-shot) | 31.52 | +| TruthfulQA (0-shot) | 34.65 | +| Winogrande (5-shot) | 65.43 | +| GSM8K (5-shot) | 0.53 | +| DROP (3-shot) | 30.75 | diff --git a/config.json b/config.json new file mode 100644 index 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