The Hugging Face Hub represents an extensive repository housing over 1 million models designed for text, image, audio, and video processing. When selecting an appropriate model for your application, consider these critical factors:

Hugging Face Models:
https://huggingface.co/models

Selecting a model depends on:

• Architectural foundation (representation vs. generative capabilities).
  
• Model size and computational requirements.
  
• Performance benchmarks and efficacy metrics.
  
• Task specialization and compatibility.
  
• Multilingual support.
  
• ...
  

For comparative analysis of embedding models across languages, the Hugging Face Embedding Leaderboard provides comprehensive benchmarking data:
https://huggingface.co/spaces/mteb/leaderboard

You can use the Hugging Face CLI to download a model:

$ huggingface-cli download microsoft/DialoGPT-small

Python code:

$ vi llm-transformers.py

# import the model and the tokenizer objects from the Transformers library
from transformers import AutoModelForCausalLM, AutoTokenizer

# load the model
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")

# load the model's tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")

# tokenize the input
input_ids = tokenizer.encode("Hello!" + tokenizer.eos_token, return_tensors='pt')

# generate the text
output = model.generate(
    input_ids=input_ids,
    max_new_tokens=50
)

# decode generated tokens
print(tokenizer.decode(output[0]))

Run the Python script:

$ python3 llm-transformers.py

Output:

Hello!<|endoftext|>Hi!<|endoftext|>

Python code:

$ vi llm-transformers-pipeline.py

# import the model and the tokenizer objects from the Transformers library
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# load the model
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")

# load the model's tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")

# create a pipeline object for the "text-generation" task
generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=50
)

# prompt pipeline with some initial text to generate more text
output = generator("Hello!" + tokenizer.eos_token)

print(output[0]["generated_text"])

Run the Python script:

$ python3 llm-transformers-pipeline.py

Output:

Hello!<|endoftext|>Hi!

Download this model:

$ wget https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/resolve/main/Phi-3-mini-4k-instruct-q4.gguf

Python code:

$ vi llm-llama-cpp.py

from llama_cpp import Llama
model = Llama(model_path="./Phi-3-mini-4k-instruct-q4.gguf")

prompt = """
Question: What's 1+1?
"""

output = model(
    prompt,
    max_tokens=50, # limits the length of the generated text.
    temperature=0, # controls the randomness of the output. Lower values are more deterministic.
    top_p=1, # (range should be (0, 1]). controls diversity of the selection of the tokens. Lower values means selecting the most probable tokens.
    echo=True # includes the prompt in the output if True.
)
print(output["choices"][0]["text"])

Run the Python script:

$ python3 llm-llama-cpp.py

Output:

Question: What's 1+1?
<|assistant|> 1+1 equals 2.

ChatGPT (OpenAI) is a proprietary model. The model can be accessed through OpenAI's API.
You need to sign-up and create an API key here: https://platform.openai.com/api-keys
The API key will be used to communicate with OpenAI's API.

• Try the model using curl:
  

  curl https://api.openai.com/v1/chat/completions \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer YOUR_API_KEY" \
    -d '{
      "model": "gpt-4o-mini",
      "store": true,
      "messages": [
        {"role": "user", "content": "What is 1+1?"}
      ]
    }'

  Output:
  

  {
    "id": "chatcmpl-BWREFCaHDdorA60Q4ufKWGZ9yY70Z",
    "object": "chat.completion",
    "model": "gpt-4o-mini-2024-07-18",
    "choices": [
      {
        "index": 0,
        "message": {
          "role": "assistant",
          "content": "1 + 1 equals 2.",
          "refusal": null,
          "annotations": []
        },
        "logprobs": null,
        "finish_reason": "stop"
      }
    ],
    "usage": {
      "prompt_tokens": 14,
      "completion_tokens": 9,
      "total_tokens": 23,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      }
    }
  }

• Try the model using Python:
  
  Install the OpenAI Python SDK:
  

  $ pip install openai

  Check OpenAI Python SDK installation:
  

  $ pip show openai

  Name: openai
  Version: 1.76.0
  ...

  Python code:
  

  $ vi llm-gpt.py

  import openai
  
  openai.api_key = "YOUR_API_KEY"
  
  completion = openai.chat.completions.create(
    model="gpt-4o-mini",
    store=True,
    messages=[
      {"role": "user", "content": "What is 1+1?"}
    ]
  )
  
  print(completion.choices[0].message);

  Run the Python script:
  

  $ python3 llm-gpt.py

  Output:
  

  ChatCompletionMessage(
      content='1 + 1 equals 2.',
      refusal=None,
      role='assistant',
      annotations=[],
      audio=None,
      function_call=None,
      tool_calls=None
  )

There are a few parameters that can affect the output of the model:

• Context Length:
  A model has a context length (a.k.a. the context window, context size, token limit):
  
  • The context length represents the maximum number of tokens the model can process.
    
  • Generative models are autoregressive, so the current context length will increase as new tokens are generated.
    
  
  
• return_full_text:
  If set to "False", only the model output is returned.
  Otherwise, the full text is returned; including the user prompt.
  
  
• max_new_tokens:
  It sets the maximum number of tokens the model can generate.
  
  
• do_sample:
  The model decides the probability of all possible values ​​of the next token. It sorts the next possible tokens based on their probability of being chosen.
  
  If the "do_sample" parameter is set to "False", the model selects the most probable next token; this leads to a more predictable and consistent response. Otherwise, the model will sample from the probability distribution, leading to a wider variety of possible token outputs.
  
  When we set the "do_sample" parameter to true, we can also use the "temperature" parameter to make the output more "random". Hence we can get different output for the same prompt.
  
  
• temperature:
  It controls the probability that the model can choose less likely tokens.
  
  When we set the "temperature" parameter to 0 (deterministic), the model should always generate the same response when given the same prompt.
  
  The closer the value of the "temperature" parameter is to 1 (high randomness), the more likely we are to get a random output.
  

To save a model, tokenizer, and configuration files, we can use the "save_pretrained" method from the Hugging Face Transformers library.

Ideally, you will save all related files in the same folder.

Note that saving the model also saves its configuration file.

• Save the model and its associated configuration files:
  
  Python code:
  

  $ vi llm-save-model.py

  from transformers import AutoModelForCausalLM
  
  model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
  
  path = "./models/microsoft/model/dialogpt-small"
  
  # model serialization
  model.save_pretrained(path)

  Run the Python script:
  

  $ python3 llm-save-model.py

  This will create a directory containing:
  

  $ ls -1 models/microsoft/model/dialogpt-small/

  config.json
  generation_config.json
  model.safetensors

• Save the model tokenizer files:
  
  Python code:
  

  $ vi llm-save-tokenizer.py

  from transformers import AutoTokenizer
  
  tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
  
  path = "./models/microsoft/tokenizer/dialogpt-small"
  
  # tokenizer serialization
  tokenizer.save_pretrained(path)

  Run the Python script:
  

  $ python3 llm-save-tokenizer.py

  This will create a directory containing:
  

  $ ls -1 models/microsoft/tokenizer/dialogpt-small/

  merges.txt
  special_tokens_map.json
  tokenizer.json
  tokenizer_config.json
  vocab.json

• Save only the model configuration file:
  
  Python code:
  

  $ vi llm-save-model-config.py

  from transformers import AutoConfig
  
  config = AutoConfig.from_pretrained("microsoft/DialoGPT-small")
  
  path = "./models/microsoft/config/dialogpt-small"
  
  # configuration serialization
  config.save_pretrained(path)

  Run the Python script:
  

  $ python3 llm-save-model-config.py

  This will create a directory containing:
  

  $ ls -1 models/microsoft/config/dialogpt-small/

  config.json

Files:

• config.json: The configuration file of the model.
  

  {
    "architectures": [
      "GPT2LMHeadModel"
    ],
  ...
    "transformers_version": "4.51.3",
    "vocab_size": 50257
  }

  
  
• tokenizer_config.json: The configuration file of the tokenizer.
  

  {
    "add_bos_token": false,
    "add_prefix_space": false,
    "added_tokens_decoder": {
      "50256": {
        "content": "<|endoftext|>",
        "lstrip": false,
        "normalized": true,
        "rstrip": false,
        "single_word": false,
        "special": true
      }
    },
    "bos_token": "<|endoftext|>",
    "chat_template": "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}",
    "clean_up_tokenization_spaces": true,
    "eos_token": "<|endoftext|>",
    "errors": "replace",
    "extra_special_tokens": {},
    "model_max_length": 1024,
    "pad_token": null,
    "tokenizer_class": "GPT2Tokenizer",
    "unk_token": "<|endoftext|>"
  }

  
  
• vocab.json, tokenizer.json: contain the vocabulary and the mapping of tokens to IDs.
  
  
• special_tokens_map.json: contains the mapping of special tokens used by the tokenizer.
  
  
• model.safetensors: contains the model's weights.
  
  
• generation_config.json, merges.txt

To load the saved model, tokenizer and configuration files, we can use the "from_pretrained" method from the Hugging Face Transformers library.

Ideally, you will have saved all related files in the same folder.

Python code:

$ vi llm-load-model-tokenizer-config.py

from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig

model_path = "./models/microsoft/model/dialogpt-small"
tokenizer_path = "./models/microsoft/tokenizer/dialogpt-small"
config_path = "./models/microsoft/config/dialogpt-small"

model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
config = AutoConfig.from_pretrained(config_path)

print(model)
print(tokenizer)
print(config)

Run the Python script:

$ python3 llm-load-model-tokenizer-config.py

Output:

GPT2LMHeadModel(
  (transformer): GPT2Model(
    (wte): Embedding(50257, 768)
    (wpe): Embedding(1024, 768)
...
  )
  (lm_head): Linear(in_features=768, out_features=50257, bias=False)
)

GPT2TokenizerFast(
    name_or_path='./models/microsoft/tokenizer/dialogpt-small',
    vocab_size=50257,
    model_max_length=1024,
...
    special_tokens={'bos_token': '<|endoftext|>', 'eos_token': '<|endoftext|>', 'unk_token': '<|endoftext|>'},
    added_tokens_decoder={50256: AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),}
)

GPT2Config {
  "architectures": [
    "GPT2LMHeadModel"
  ],
...
  "transformers_version": "4.51.3",
  "vocab_size": 50257
}