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, AutoModelForMultimodalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Xenova HF Staff commited on Feb 16, 2024

Commit

2dfca24

verified ·

1 Parent(s): 0238ba5

Add default chat template to tokenizer_config.json

Browse files

[Automated] This PR adds the default chat template to the tokenizer config, allowing the model to be used with the new conversational widget (see [PR](https://github.com/huggingface/huggingface.js/pull/457)).

If the default is not appropriate for your model, please set `tokenizer.chat_template` to an appropriate template. See https://huggingface.co/docs/transformers/main/chat_templating for more information.

Files changed (1) hide show

tokenizer_config.json +11 -1

tokenizer_config.json CHANGED Viewed

	@@ -1 +1,11 @@
1	- {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "cls_token": "<cls>", "mask_token": "<mask>", "special_tokens_map_file": "/gdrive/My Drive/Colab Notebooks/donnees/textgen_fr/chatbot/gpt2-medium-frtok/special_tokens_map.json", "full_tokenizer_file": null}

+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "unk_token": "<unk>",
+  "pad_token": "<pad>",
+  "cls_token": "<cls>",
+  "mask_token": "<mask>",
+  "special_tokens_map_file": "/gdrive/My Drive/Colab Notebooks/donnees/textgen_fr/chatbot/gpt2-medium-frtok/special_tokens_map.json",
+  "full_tokenizer_file": null,
+  "chat_template": "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}"
+}