Instructions to use cedpsam/chatbot_fr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cedpsam/chatbot_fr with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cedpsam/chatbot_fr")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cedpsam/chatbot_fr")
model = AutoModelForCausalLM.from_pretrained("cedpsam/chatbot_fr")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cedpsam/chatbot_fr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cedpsam/chatbot_fr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cedpsam/chatbot_fr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cedpsam/chatbot_fr

SGLang

How to use cedpsam/chatbot_fr with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "cedpsam/chatbot_fr" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cedpsam/chatbot_fr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "cedpsam/chatbot_fr" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cedpsam/chatbot_fr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cedpsam/chatbot_fr with Docker Model Runner:
```
docker model run hf.co/cedpsam/chatbot_fr
```

a dialoggpt model trained on french opensubtitles with custom tokenizer

trained with this notebook https://colab.research.google.com/drive/1pfCV3bngAmISNZVfDvBMyEhQKuYw37Rl#scrollTo=AyImj9qZYLRi&uniqifier=3

config from microsoft/DialoGPT-medium dataset generated from 2018 opensubtitle downloaded from opus folowing these guidelines https://github.com/PolyAI-LDN/conversational-datasets/tree/master/opensubtitles with this notebook https://colab.research.google.com/drive/1uyh3vJ9nEjqOHI68VD73qxt4olJzODxi#scrollTo=deaacv4XfLMk

How to use

Now we are ready to try out how the model works as a chatting partner!

import torch
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("cedpsam/chatbot_fr")

model = AutoModelWithLMHead.from_pretrained("cedpsam/chatbot_fr")

for step in range(6):
    # encode the new user input, add the eos_token and return a tensor in Pytorch
    new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
    # print(new_user_input_ids)

    # append the new user input tokens to the chat history
    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids

    # generated a response while limiting the total chat history to 1000 tokens, 
    chat_history_ids = model.generate(
        bot_input_ids, max_length=1000,
        pad_token_id=tokenizer.eos_token_id,
        top_p=0.92, top_k = 50
    )
    
    # pretty print last ouput tokens from bot
    print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))

Downloads last month: 9

Safetensors

Model size

0.4B params

Tensor type

F32

cedpsam
/

chatbot_fr

a dialoggpt model trained on french opensubtitles with custom tokenizer

How to use

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