Create README.md

README.md

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+```python
+!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git
+!pip install -q datasets bitsandbytes einops wandb sentencepiece transformers_stream_generator tiktoken
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("TinyPixel/qwen-1.8B-guanaco", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("TinyPixel/qwen-1.8B-guanaco", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
+
+device = "cuda:0"
+
+from transformers import StoppingCriteria, StoppingCriteriaList
+
+stop_token_ids = [[14374, 11097, 25], [14374, 21388, 25]]
+stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]
+
+from transformers import StoppingCriteria, StoppingCriteriaList
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        for stop_ids in stop_token_ids:
+            if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
+                return True
+        return False
+
+stopping_criteria = StoppingCriteriaList([StopOnTokens()])
+
+text = '''### Human: what is the difference between a dog and a cat on a biological level?
+### Assistant:'''
+
+inputs = tokenizer(text, return_tensors="pt").to(device)
+outputs = model.generate(**inputs,
+              max_new_tokens=512,
+              stopping_criteria=stopping_criteria,
+              do_sample=True,
+              top_p=0.95,
+              temperature=0.7,
+              top_k=50)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=False)
+```