Update README.md

README.md

@@ -33,42 +33,7 @@ pip install torch==2.0.0
 pip install einops==0.6.1
 ```
 
-```python
-import torch
-from transformers import pipeline
-
-generate_text = pipeline(
-    model="h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b",
-    torch_dtype=torch.bfloat16,
-    trust_remote_code=True,
-    use_fast=False,
-    device_map={"": "cuda:0"},
-)
-
-res = generate_text(
-    "Why is drinking water so healthy?",
-    min_new_tokens=2,
-    max_new_tokens=1024,
-    do_sample=False,
-    num_beams=1,
-    temperature=float(0.3),
-    repetition_penalty=float(1.2),
-    renormalize_logits=True
-)
-print(res[0]["generated_text"])
-```
-
-You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
-
-```python
-print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
-```
-
-```bash
-<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
-```
-
-Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
+Download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer:
 
 
 ```python
@@ -103,6 +68,15 @@ res = generate_text(
 print(res[0]["generated_text"])
 ```
 
+You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
+
+```python
+print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
+```
+
+```bash
+<|prompt|>Why is drinking water so healthy?<|endoftext|><|answer|>
+```
 
 You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: