183 lines
5.5 KiB
Markdown
183 lines
5.5 KiB
Markdown
---
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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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