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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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<br> |
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<br> |
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## Example Usage |
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### 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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### Code example: |
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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-v1.5" |
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output_file_path = "./SynthIA-7B-v1.5-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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# [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-v1.5) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 54.8 | |
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| ARC (25-shot) | 62.71 | |
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| HellaSwag (10-shot) | 83.37 | |
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| MMLU (5-shot) | 63.48 | |
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| TruthfulQA (0-shot) | 51.32 | |
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| Winogrande (5-shot) | 79.24 | |
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| GSM8K (5-shot) | 17.44 | |
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| DROP (3-shot) | 26.01 | |
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