Migel Tissera commited on
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actor/README.md ADDED
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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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+ ## Example Usage
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+
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+ ### Prompt format:
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+
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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 insulin synthesized?
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+ ASSISTANT:
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+ ```
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+
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+ ### Code example:
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+
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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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+
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+ model_path = "migtissera/SynthIA-7B-v2.0"
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+ output_file_path = "./SynthIA-7B-v2.0-conversations.jsonl"
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+
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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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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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+
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+
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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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+
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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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+
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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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+
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+
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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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+
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+
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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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+
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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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+ ```
actor/config.json ADDED
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+ {
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+ "_name_or_path": "/home/ubuntu/llm/Mistral-7B-v0.1",
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+ "architectures": [
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+ "MistralForCausalLM"
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+ ],
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "max_position_embeddings": 32768,
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+ "model_type": "mistral",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 10000.0,
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+ "sliding_window": 4096,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.34.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
actor/generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 1,
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+ "transformers_version": "4.34.0.dev0"
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+ }
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