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license: apache-2.0 |
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# Synthia-7B-v3.0 |
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SynthIA-7B-v3.0 (Synthetic Intelligent Agent) is a Mistral-7B model trained with guidance on Orca-2 paper. It has been fine-tuned for instruction following as well as having long-form conversations. SynthIA-3.0 dataset contains the Generarized Tree-of-Thought prompt plus 10 more new long-form system contexts. However, in the training phase the system context was removed as suggested in Orca-2 paper. |
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 |
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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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## Evaluation |
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We evaluated Synthia-7B-v3.0 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). Section to follow. |
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|**Task**|**Metric**|**Value**| |
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|*arc_challenge*|acc_norm|| |
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|*hellaswag*|acc_norm|| |
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|*mmlu*|acc_norm|| |
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|*truthfulqa_mc*|mc2|| |
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|**Total Average**|-||| |
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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: What is the difference between an Orca, Dolphin and a Seal? |
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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-v3.0" |
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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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#### 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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