Update README.md

README.md

@@ -11,11 +11,6 @@ pipeline_tag: text-generation
 
 Deer will also be available in larger models size.
 
-## Model Overview
-`deer-3b` is a 3 billion parameter causal language model created that is derived from 
-[Blooms’s] 3B model and fine-tuned 
-on a ~15K instructions. 
-
 ## Usage
 
 To use the model with the `transformers` library on a machine with GPUs.
@@ -33,42 +28,6 @@ res = generate_text("Explain to me the difference between nuclear fission and fu
 print(res[0]["generated_text"])
 ```
 
-### 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.
-
-```python
-import torch
-from transformers import pipeline
-generate_text = pipeline(model="PSanni/Deer-3b", 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:
-
-```python
-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:
-
-```python
-print(llm_chain.predict(instruction="Give me list of morning exercises.").lstrip())
-```
-
 ### Note:
 
 Kindly note that the model isn't attuned to human preferences and could generate unsuitable, unethical, biased, and toxic responses.