Instructions to use BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1 with Transformers:

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

pipe = pipeline("text-generation", model="BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1")
model = AutoModelForCausalLM.from_pretrained("BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1

SGLang

How to use BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1 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 "BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1" \
    --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": "BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1",
		"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 "BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1" \
        --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": "BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1 with Docker Model Runner:
```
docker model run hf.co/BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1
```

Mae'r model LLM yn seiliedig ar BangorAI/mistral-7b-cy-tokenizer-train-6, sef y model Mistral-7B, gyda estyniad yn y tocynnydd ac hyfforddiant parhaus ar gyfer y Gymraeg.

Cafodd y model hyfforddiant cywrain pellach ar ddata Cofnod y Cynulliad a ddarparir gan TechIaith.

Fformat Sgwrs

Mae'r hyfforddiant cywrain wedi defnyddio'r fformat canlynol ar gyfer trosi o'r Saesneg i'r Gymraeg (a'r naill ffordd i'r llall).

Cyfieithwch y testun Saesneg canlynol i'r Gymraeg.
### Saesneg:
{prompt}

### Cymraeg:

Hawlfraint

Mae'r data Cofnod y Cynulliad dan drywdded Llywodraeth Agored.

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BangorAI
/

Cyfieithu-Mistral-7b-tocynnydd-1

Fformat Sgwrs

Hawlfraint

Dataset used to train BangorAI/Cyfieithu-Mistral-7b-tocynnydd-1