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Model: migtissera/SynthIA-7B-v1.3 Source: Original Platform
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---
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license: apache-2.0
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pipeline_tag: text-generation
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language:
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- en
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library_name: transformers
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---
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SynthIA-7B-v1.3: Base model is Mistral-7B-v0.1
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All SynthIA models are uncensored. Please use it with caution and with best intentions. You are responsible for how you use SynthIA.
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To evoke generalized Tree of Thought + Chain of Thought reasoning, you may use the following system message:
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```
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Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
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```
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# SynthIA-7B-v1.3
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SynthIA (Synthetic Intelligent Agent) 7B-v1.3 is a Mistral-7B-v0.1 model trained on Orca style datasets. It has been fine-tuned for instruction following as well as having long-form conversations.
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<br>
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<br>
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<br>
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#### License Disclaimer:
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This model is released under Apache 2.0, and comes with no warranty or gurantees of any kind.
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<br>
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## Evaluation
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We evaluated SynthIA-7B-v1.3 on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.
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Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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|:------:|:--------:|:-------:|
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|**Task**|**Metric**|**Value**|
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|*arc_challenge*|acc_norm|0.6237|
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|*hellaswag*|acc_norm|0.8349|
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|*mmlu*|acc_norm|0.6232|
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|*truthfulqa_mc*|mc2|0.5125|
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|**Total Average**|-|**0.6485**||
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<br>
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## Example Usage
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### Here is prompt format:
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```
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SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
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USER: How is a rocket launched from the surface of the earth to Low Earth Orbit?
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ASSISTANT:
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```
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### Below shows a code example on how to use this model:
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```python
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import torch, json
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_path = "migtissera/SynthIA-7B-v1.3"
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output_file_path = "./SynthIA-7B-conversations.jsonl"
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype=torch.float16,
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device_map="auto",
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load_in_8bit=False,
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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def generate_text(instruction):
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tokens = tokenizer.encode(instruction)
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tokens = torch.LongTensor(tokens).unsqueeze(0)
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tokens = tokens.to("cuda")
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instance = {
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"input_ids": tokens,
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"top_p": 1.0,
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"temperature": 0.75,
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"generate_len": 1024,
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"top_k": 50,
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}
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length = len(tokens[0])
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with torch.no_grad():
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rest = model.generate(
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input_ids=tokens,
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max_length=length + instance["generate_len"],
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use_cache=True,
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do_sample=True,
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top_p=instance["top_p"],
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temperature=instance["temperature"],
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top_k=instance["top_k"],
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num_return_sequences=1,
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)
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output = rest[0][length:]
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string = tokenizer.decode(output, skip_special_tokens=True)
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answer = string.split("USER:")[0].strip()
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return f"{answer}"
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conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
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while True:
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user_input = input("You: ")
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llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
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answer = generate_text(llm_prompt)
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print(answer)
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conversation = f"{llm_prompt}{answer}"
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json_data = {"prompt": user_input, "answer": answer}
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## Save your conversation
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with open(output_file_path, "a") as output_file:
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output_file.write(json.dumps(json_data) + "\n")
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```
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<br>
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#### Limitations & Biases:
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While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
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Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
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Exercise caution and cross-check information when necessary. This is an uncensored model.
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<br>
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### Citiation:
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Please kindly cite using the following BibTeX:
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```
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@misc{SynthIA-7B-v1.3,
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author = {Migel Tissera},
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title = {SynthIA-7B-v1.3: Synthetic Intelligent Agent},
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year = {2023},
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publisher = {GitHub, HuggingFace},
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journal = {GitHub repository, HuggingFace repository},
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howpublished = {\url{https://huggingface.co/migtissera/Synthia-13B},
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}
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```
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```
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@misc{mukherjee2023orca,
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title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
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author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
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year={2023},
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eprint={2306.02707},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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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_migtissera__SynthIA-7B-v1.3)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 57.11 |
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| ARC (25-shot) | 62.12 |
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| HellaSwag (10-shot) | 83.45 |
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| MMLU (5-shot) | 62.65 |
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| TruthfulQA (0-shot) | 51.37 |
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| Winogrande (5-shot) | 78.85 |
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| GSM8K (5-shot) | 17.59 |
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| DROP (3-shot) | 43.76 |
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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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||||
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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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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.norm.weight": "pytorch_model-00002-of-00002.bin"
|
||||
}
|
||||
}
|
||||
5
special_tokens_map.json
Normal file
5
special_tokens_map.json
Normal file
@@ -0,0 +1,5 @@
|
||||
{
|
||||
"bos_token": "<s>",
|
||||
"eos_token": "</s>",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
40
tokenizer_config.json
Normal file
40
tokenizer_config.json
Normal file
@@ -0,0 +1,40 @@
|
||||
{
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": null,
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": true
|
||||
}
|
||||
Reference in New Issue
Block a user