Hugging Face - Part3 - Inference with Code Llama using LangChain

In the field of understanding and working with human language (NLP), Hugging Face is a key platform that provides many pre-trained models for different tasks. With Transformers, LangChain, and Python developers can easily use Hugging Face's models on their own computers for quick processing. Using LangChain offers a streamlined and user-friendly approach to tapping into the capabilities of pre-trained language models. In this blog post we focus on how to inference with Code Llama - Instruct model from Hugging Face locally using LangChain. 

You can access the Python script in my GitHub repository:
https://github.com/vineethac/huggingface/tree/main/4-codellama_with_langchain

To initiate inference with Code Llama, developers can start by specifying the desired model using its identifier, such as MODEL_ID = "codellama/CodeLlama-7b-Instruct-hf". Transformers simplifies the process by providing a unified interface with the familiar Python programming language, allowing users to effortlessly initialize the model and tokenizer.

Once the model and tokenizer are set up, developers can leverage LangChain's HuggingFacePipeline class to create a text generation pipeline. This pipeline, defined with parameters like max_new_tokens and repetition_penalty, becomes a powerful tool for local inferencing. By combining this pipeline with LangChain's PromptTemplate, developers can easily construct prompts and invoke the entire chain to generate responses. This streamlined process facilitates local inferencing with Code Llama, empowering developers to leverage Hugging Face's models for a wide range of natural language processing tasks in their Python applications. 

Example

root@hf-3:/codellama# python3 codellama_langchain.py
tokenizer_config.json: 100%|█████████████████████████████████████████████████████████| 749/749 [00:00<00:00, 3.57MB/s]
tokenizer.model: 100%|█████████████████████████████████████████████████████████████| 500k/500k [00:00<00:00, 4.48MB/s]
tokenizer.json: 100%|████████████████████████████████████████████████████████████| 1.84M/1.84M [00:00<00:00, 6.13MB/s]
special_tokens_map.json: 100%|███████████████████████████████████████████████████████| 411/411 [00:00<00:00, 1.86MB/s]
config.json: 100%|███████████████████████████████████████████████████████████████████| 646/646 [00:00<00:00, 3.40MB/s]
model.safetensors.index.json: 100%|██████████████████████████████████████████████| 25.1k/25.1k [00:00<00:00, 68.2MB/s]
model-00001-of-00002.safetensors: 100%|██████████████████████████████████████████| 9.98G/9.98G [01:50<00:00, 90.0MB/s]
model-00002-of-00002.safetensors: 100%|██████████████████████████████████████████| 3.50G/3.50G [00:39<00:00, 89.5MB/s]
Downloading shards: 100%|███████████████████████████████████████████████████████████████| 2/2 [02:30<00:00, 75.16s/it]
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████| 2/2 [00:05<00:00,  2.86s/it]
generation_config.json: 100%|█████████████████████████████████████████████████████████| 116/116 [00:00<00:00, 110kB/s]
Ask codellama: given two unsorted integer lists. merge the two lists, sort the merged list, and find median using python. consider the length of the merged list while finding the median value.
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
 Here is a possible solution to the problem:
def merge_and_find_median(list1, list2):
   # Merge the two lists
   merged_list = list1 + list2
   # Sort the merged list
   merged_list.sort()
   # Find the median value
   if len(merged_list) % 2 == 0:
       # Even number of elements in the merged list
       median = (merged_list[len(merged_list) // 2 - 1] + merged_list[len(merged_list) // 2]) / 2
   else:
       # Odd number of elements in the merged list
       median = merged_list[len(merged_list) // 2]
   return median
Explanation:
* First, we merge the two lists by concatenating them.
* Then, we sort the merged list using the `sort()` method.
* Next, we check whether the length of the merged list is even or odd. If it's even, we take the average of the middle two elements of the list. If it's odd, we simply take the middle element as the median.
* Finally, we return the median value.
Note that this solution assumes that both input lists are sorted in ascending order. If they are not sorted, you may need to add additional code to sort them before merging and finding the median.</s>
Ask codellama: /bye
root@hf-3:/codellama#

Hope it was useful. Cheers!