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README.md ADDED
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+ ---
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+ tags:
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+ - autotrain
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+ - text-generation-inference
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+ - text-generation
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+ - peft
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+ library_name: transformers
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+ base_model: Qwen/Qwen2-7B-Instruct
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+ widget:
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+ - messages:
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+ - role: user
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+ content: What is your favorite condiment?
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+ license: other
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+ ---
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+
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+ # Model Trained Using AutoTrain
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+
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+ This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
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+
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+ # Usage
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+
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+ ```python
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+
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_path = "PATH_TO_THIS_REPO"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_path)
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_path,
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+ device_map="auto",
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+ torch_dtype='auto'
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+ ).eval()
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+
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+ # Prompt content: "hi"
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+ messages = [
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+ {"role": "user", "content": "hi"}
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+ ]
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+
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+ input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
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+ output_ids = model.generate(input_ids.to('cuda'))
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+ response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
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+
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+ # Model response: "Hello! How can I assist you today?"
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+ print(response)
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+ ```
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+ "bias": "none",
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+ "inference_mode": true,
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+ "megatron_core": "megatron.core",
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+ "modules_to_save": [
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+ "classifier",
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+ "score"
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+ ],
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+ "peft_type": "LORA",
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+ "r": 16,
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+ "target_modules": [
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+ ],
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+ "task_type": "SEQ_CLS",
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+ "use_dora": false,
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+ }
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+ "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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+ "model": "Qwen/Qwen2-7B-Instruct",
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+ "project_name": "Qwen2-7B-Instruct-90s-Commercials-v2",
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+ "data_path": "Qwen2-7B-Instruct-90s-Commercials-v2/autotrain-data",
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+ }
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