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Update README.md
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README.md
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@@ -25,28 +25,26 @@ ASSISTANT:
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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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output_file_path = "./SynthIA-7B-v2.0-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=
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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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@@ -54,7 +52,6 @@ def generate_text(instruction):
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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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### Code example:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch, json
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# model path
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model_path = "NurtureAI/SynthIA-7B-v2.0-16k"
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output_file_path = "./SynthIA-7B-v2.0-conversations.jsonl"
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device_map = {"": "cuda"}
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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=device_map,
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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
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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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"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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