Instructions to use Finisha-F-scratch/melta-conversation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Finisha-F-scratch/melta-conversation with Transformers:

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

pipe = pipeline("text-generation", model="Finisha-F-scratch/melta-conversation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Finisha-F-scratch/melta-conversation")
model = AutoModelForSeq2SeqLM.from_pretrained("Finisha-F-scratch/melta-conversation")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Finisha-F-scratch/melta-conversation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Finisha-F-scratch/melta-conversation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Finisha-F-scratch/melta-conversation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Finisha-F-scratch/melta-conversation

SGLang

How to use Finisha-F-scratch/melta-conversation 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 "Finisha-F-scratch/melta-conversation" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Finisha-F-scratch/melta-conversation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Finisha-F-scratch/melta-conversation" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Finisha-F-scratch/melta-conversation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Finisha-F-scratch/melta-conversation with Docker Model Runner:
```
docker model run hf.co/Finisha-F-scratch/melta-conversation
```

🌸 Melta-conversation 🩷

🦋 Melta ✨

Melta est un SLM (small language model), Créé à partir de l'affinage sur un autre SLM from scratch : Tesity-T5. Il a pour tâche de jouer le rôle de notre ancien bot discord mignon : Melta27.

Melta-conversation est optimisée pour les courtes conversations et les questions autour de l'ancien bot discord, Et de ses souvenirs.