107 lines
3.1 KiB
Markdown
107 lines
3.1 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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<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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