commit files to HF hub

README.md

@@ -28,7 +28,8 @@ import torch
 from transformers import pipeline
 
 generate_text = pipeline(model="h2oai/h2ogpt-oasst1-512-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
-res = generate_text("Why is drinking water so healthy?")
+
+res = generate_text("Why is drinking water so healthy?", max_new_tokens=100)
 print(res[0]["generated_text"])
 ```
 
@@ -39,63 +40,13 @@ store it alongside your notebook, and construct the pipeline yourself from the l
 import torch
 from h2oai_pipeline import H2OTextGenerationPipeline
 from transformers import AutoModelForCausalLM, AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained("h2oai/h2ogpt-oasst1-512-12b", padding_side="left")
-model = AutoModelForCausalLM.from_pretrained("h2oai/h2ogpt-oasst1-512-12b", device_map="auto", torch_dtype=torch.bfloat16)
 
+tokenizer = AutoTokenizer.from_pretrained("h2oai/h2ogpt-oasst1-512-12b", padding_side="left")
+model = AutoModelForCausalLM.from_pretrained("h2oai/h2ogpt-oasst1-512-12b", torch_dtype=torch.bfloat16, device_map="auto")
 generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
-```
-
-### LangChain Usage
-
-To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned 
-and the default for the pipeline is to only return the new text.
-
-```
-import torch
-from transformers import pipeline
-
-generate_text = pipeline(model="h2oai/h2ogpt-oasst1-512-12b", torch_dtype=torch.bfloat16,
-                         trust_remote_code=True, device_map="auto", return_full_text=True)
-```
-
-You can create a prompt that either has only an instruction or has an instruction with context:
-
-```
-from langchain import PromptTemplate, LLMChain
-from langchain.llms import HuggingFacePipeline
-
-# template for an instrution with no input
-prompt = PromptTemplate(
-    input_variables=["instruction"],
-    template="{instruction}")
-
-# template for an instruction with input
-prompt_with_context = PromptTemplate(
-    input_variables=["instruction", "context"],
-    template="{instruction}\n\nInput:\n{context}")
-
-hf_pipeline = HuggingFacePipeline(pipeline=generate_text)
-
-llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
-llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context)
-```
-
-Example predicting using a simple instruction:
 
-```
-print(llm_chain.predict(instruction="Why is drinking water so healthy?").lstrip())
-```
-
-Example predicting using an instruction with context:
-
-```
-context = """Model A: AUC=0.8
-Model from Driverless AI: AUC=0.95
-Model C: AUC=0.6
-Model D: AUC=0.7
-"""
-
-print(llm_context_chain.predict(instruction="Which model performs best?", context=context).lstrip())
+res = generate_text("Why is drinking water so healthy?", max_new_tokens=100)
+print(res[0]["generated_text"])
 ```
 
 ## Model Architecture