Instructions to use gustavecortal/fr-boris-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gustavecortal/fr-boris-8bit with Transformers:

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

pipe = pipeline("text-generation", model="gustavecortal/fr-boris-8bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gustavecortal/fr-boris-8bit")
model = AutoModelForCausalLM.from_pretrained("gustavecortal/fr-boris-8bit")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gustavecortal/fr-boris-8bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gustavecortal/fr-boris-8bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gustavecortal/fr-boris-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/gustavecortal/fr-boris-8bit

SGLang

How to use gustavecortal/fr-boris-8bit 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 "gustavecortal/fr-boris-8bit" \
    --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": "gustavecortal/fr-boris-8bit",
		"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 "gustavecortal/fr-boris-8bit" \
        --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": "gustavecortal/fr-boris-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gustavecortal/fr-boris-8bit with Docker Model Runner:
```
docker model run hf.co/gustavecortal/fr-boris-8bit
```

YAML Metadata Error:"datasets[1]" with value "The Pile" is not valid. If possible, use a dataset id from https://hf.co/datasets.

Quantized Cedille/fr-boris with 8-bit weights

This is a version of Cedille's GPT-J (fr-boris) with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti). Inspired by GPT-J 8bit.

Here's how to run it:

This model can be easily loaded using the GPTJForCausalLM functionality:

from transformers import GPTJForCausalLM
model = GPTJForCausalLM.from_pretrained("gustavecortal/fr-boris-8bit")

fr-boris

Boris is a 6B parameter autoregressive language model based on the GPT-J architecture and trained using the mesh-transformer-jax codebase.

Boris was trained on around 78B tokens of French text from the C4 dataset.

gustavecortal
/

fr-boris-8bit

Quantized Cedille/fr-boris with 8-bit weights

fr-boris

Links