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Model: VMware/open-llama-0.7T-7B-open-instruct-v1.1 Source: Original Platform
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README.md
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---
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license: cc
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datasets:
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- VMware/open-instruct-v1.1-oasst-dolly-hhrlhf
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language:
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- en
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library_name: transformers
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pipeline_tag: conversational
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---
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# VMware/open-llama-0.7T-7B-open-instruct-v1.1
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---
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# UPDATE: Final Version Now Available!
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Please use the final version: [Open LLaMA 7B Open Instruct](https://huggingface.co/VMware/open-llama-7b-open-instruct)
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---
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## License
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- <b>Commercially Viable </b>
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- 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
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- Language Model ([openlm-research/open_llama_7b_700bt_preview](https://huggingface.co/openlm-research/open_llama_7b_700bt_preview)) is under apache-2.0
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## Nomenclature
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- Model : Open-llama
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- Model trained on : 700B or 0.7 T tokens
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- Model Size: 7B parameters
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- Dataset: Open-instruct-v1.1 (oasst,dolly, hhrlhf)
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- Version: 1.1 (Alpaca prompt template)
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## Use in Transformers
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```
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import os
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = 'VMware/open-llama-0.7T-7B-open-instruct-v1.1'
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype= torch.float16, device_map = 'sequential')
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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:"
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prompt= 'Explain in simple terms how the attention mechanism of a transformer model works'
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inputt = prompt_template.format(instruction= prompt)
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input_ids = tokenizer(inputt, return_tensors="pt").input_ids.to("cuda")
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output1 = model.generate(input_ids, max_length=512)
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input_length = input_ids.shape[1]
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output1 = output1[:, input_length:]
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output= tokenizer.decode(output1[0])
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print(output)
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'''
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The attention mechanism of a transformer model is designed to help the model understand the relationship between different parts of a sentence.
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The model uses a weighted attention score to determine how much each input token contributes to the output.
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The attention score is calculated by looking at the similarity between each input token and the output token,and assigning a weight to each input token based on this similarity.
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This way, the model can better understand the relationship between different parts of a sentence and generate more accurate predictions.
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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_VMware__open-llama-0.7T-7B-open-instruct-v1.1)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 39.33 |
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| ARC (25-shot) | 46.67 |
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| HellaSwag (10-shot) | 67.67 |
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| MMLU (5-shot) | 28.55 |
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| TruthfulQA (0-shot) | 37.6 |
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| Winogrande (5-shot) | 65.43 |
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| GSM8K (5-shot) | 0.76 |
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| DROP (3-shot) | 28.61 |
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config.json
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config.json
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{
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"_name_or_path": "/home/gollapudit/peft/open_llama_700bt_open_instruct_v1.1",
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"architectures": [
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"LlamaForCausalLM"
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],
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 2048,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.28.1",
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"use_cache": true,
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"vocab_size": 32000
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}
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||||
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||||
6
special_tokens_map.json
Normal file
6
special_tokens_map.json
Normal file
@@ -0,0 +1,6 @@
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
96948
tokenizer.json
Normal file
96948
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
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Normal file
3
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Normal file
@@ -0,0 +1,3 @@
|
||||
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||||
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||||
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||||
34
tokenizer_config.json
Normal file
34
tokenizer_config.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
}
|
||||
Reference in New Issue
Block a user