update readme

README.md

@@ -59,24 +59,44 @@ chem_model.run("What is the molecular weight of tylenol?")
 
 ### 💻 Running using local LLMs.
 
-ChemCrow also supports the use of local LLMs, powered by GPT4All, which can be run on a laptop.
+ChemCrow also supports the use of local LLMs, through either GPT4All, or HuggingFace's [TGI](https://huggingface.co/docs/text-generation-inference/index).
 
-A list of supported models is provided [here](https://gpt4all.io/index.html).
+#### GPT4All
+
+To run using GPT4All, you will need to download one of the [supported models](https://gpt4all.io/index.html).
 
 ```python
 from chemcrow.agents import ChemCrow
 
 chem_model = ChemCrow(
+    model_type='gpt4all',
     model="./models/mistral-7b-instruct-v0.1.Q4_0.gguf",
-    tools_model="./models/mistral-7b-instruct-v0.1.Q4_0.gguf",
-    temp=0.1, verbose=False, max_tokens=100, n_ctx=2048
+    temp=0.1,
+    max_tokens=100,
+    verbose=False,
 )
-output = chem_model.run("What is the molecular weight of tylenol?")
+```
+
+#### TGI
 
->>> output
->>> The molecular weight of acetaminophen is 151.17 g/mol ...
+The other option is Text Generation Interface. This allows you to serve a model and run inference as an API.
+To deploy a model, you will need docker. Run it as explained [here](https://huggingface.co/docs/text-generation-inference/quicktour).
+
+```python
+from chemcrow.agents import ChemCrow
+
+agent = ChemCrow(
+    model_type='tgi',
+    model_server_url='http://server-ip-address:8080',
+    temp=0.3,
+    max_tokens=40,
+    max_iterations=3,
+).agent_executor
 ```
 
+The advantage of TGI is improved efficiency, plus easy access to any model available in HuggingFace.
+
+
 ## ✅ Citation
 Bran, Andres M., et al. "ChemCrow: Augmenting large-language models with chemistry tools." arXiv preprint arXiv:2304.05376 (2023).