commit f6ce72e081d99f916bd977e9fbcd9fa2ffe9be27 Author: ModelHub XC Date: Mon Jun 8 21:06:23 2026 +0800 初始化项目,由ModelHub XC社区提供模型 Model: VMware/open-llama-7b-v2-open-instruct 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 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 +*.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..e4684c7 --- /dev/null +++ b/README.md @@ -0,0 +1,153 @@ +--- +license: cc-by-sa-3.0 +datasets: +- VMware/open-instruct +language: +- en +library_name: transformers +pipeline_tag: text-generation +--- + +# VMware/open-llama-7B-v2-open-instruct +Instruction-tuned version of the fully trained Open LLama 7B v2 model. The model is open for COMMERCIAL USE.
+ +- This model performs better on code compared to v1 due to the improvements made on the base model by the openlm-research team. +- The instruction model is trained on an improved instruction tuning dataset compared to v1 + +**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 +- CC BY-SA-3.0 **(Commercially Viable!)** +- Base Language Model ([openlm-research/open_llama_v2_7b](https://huggingface.co/openlm-research/open_llama_v2_7b)) is under apache-2.0 +- Fine-Tuning Dataset ([VMware/open-instruct](https://huggingface.co/datasets/VMware/open-instruct)) is under cc-by-sa-3.0 + +## Datasets used for Fine-Tuning + +### Open-instruct + +**Open-instruct-v1** +- Mosaic/Dolly-HHRLHF + filtered OASST1 - cc by 3.0 + +**Subset of COT SUBMIX (FROM FLAN V2) Zeroshot examples** +- ESNLI - MIT +- ECQA - CDLA 1.0 - Sharing +- Strategy - MIT +- CREAK - MIT +- gsmk8 - MIT +- aqua - MIT +- qasc - Apache 2.0 + + +## Nomenclature + +- Model : Open-llama-v2 +- Model Size: 7B parameters +- Dataset: Open-instruct + + +## Use in Transformers + +``` +import os +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = 'VMware/open-llama-7b-v2-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 = """What is attention mechanism of a transformer model? + Write a python code to illustrate how attention works within a transformer model using numpy library. Donot use pytorch or tensorflow.""" + + +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) + +``` + + +### Output + +Sure, I can help you with that! + +Attention mechanisms in transformer models are typically implemented using the attention mechanism in the self-attention layer. Self-attention allows the model to focus on different parts of the input sequence when processing it. This is achieved by computing a set of attention weights, which are used to weigh the contribution of each input element to the output. + +Here's an example code using NumPy to illustrate how attention works in a transformer model: + +```python +import numpy as np + +def attention_weights(query, key, value, mask): + # Query, key, and value are input tensors. Mask is a tensor of zeros and ones that represents the attention mask. + # It is used to prevent the model from attending to certain positions in the input sequence if they are not relevant. + # The attention weights are the element-wise product of the query, key, and mask tensors. + # The result is a tensor of the same shape as the query tensor. + + # Compute the dot product between the query tensor and the key tensor + dot = np.matmul(query, key) + + # Compute the element-wise softmax of the dot product tensor + exp_dot = np.exp(dot) + + # Multiply the dot product and the softmax of the dot product tensors + weights = dot * exp_dot + + # Return the attention weights as a NumPy tensor + return weights + +# Define the input sequence +query = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]) +key = np.array([[0.1, 0.2], [0.3, 0.4]]) +value = np.array([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]) +mask = np.array([[False, True, True], [False, True, True]]) + +# Compute the attention weights +weights = attention_weights(query, key, value, mask) + +# Print the attention weights +print(weights) +``` + +In this example, the `attention_weights` function takes as input the query tensor, key tensor, value tensor, and mask tensor. It computes the dot product between the query and key tensors using the `np.matmul` function, and then applies a softmax function using the `np.exp` function to the element-wise dot product tensor. It then multiplies the dot product and softmax tensors using the `np.matmul` function, and returns the result as a NumPy tensor. + +The `query`, `key`, and `value` tensors represent the input sequence to the transformer model. The `mask` tensor represents the attention mask, which is used to prevent the model from attending to certain positions in the input sequence if they are not relevant. + +The output of the `attention_weights` function is a NumPy tensor that represents the attention weights for the input sequence. These weights are used by the transformer model to weigh the contribution of each input element to the output. + +I hope this helps! +
+ + +## 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-v2-open-instruct) + +| Metric | Value | +|-----------------------|---------------------------| +| Avg. | 40.34 | +| ARC (25-shot) | 39.76 | +| HellaSwag (10-shot) | 70.31 | +| MMLU (5-shot) | 35.16 | +| TruthfulQA (0-shot) | 39.53 | +| Winogrande (5-shot) | 64.33 | +| GSM8K (5-shot) | 7.43 | +| DROP (3-shot) | 25.88 | diff --git a/config.json b/config.json new file mode 100644 index 0000000..4b56774 --- /dev/null +++ b/config.json @@ -0,0 +1,23 @@ +{ + "_name_or_path": "/home/gollapudit/peft/open_llama_7b_v2_open_instruct", + 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