Instructions to use BangorAI/cyfieithydd-7b-fersiwn-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BangorAI/cyfieithydd-7b-fersiwn-3 with Transformers:

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

pipe = pipeline("text-generation", model="BangorAI/cyfieithydd-7b-fersiwn-3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BangorAI/cyfieithydd-7b-fersiwn-3")
model = AutoModelForCausalLM.from_pretrained("BangorAI/cyfieithydd-7b-fersiwn-3")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BangorAI/cyfieithydd-7b-fersiwn-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BangorAI/cyfieithydd-7b-fersiwn-3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BangorAI/cyfieithydd-7b-fersiwn-3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BangorAI/cyfieithydd-7b-fersiwn-3

SGLang

How to use BangorAI/cyfieithydd-7b-fersiwn-3 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/cyfieithydd-7b-fersiwn-3" \
    --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/cyfieithydd-7b-fersiwn-3",
		"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/cyfieithydd-7b-fersiwn-3" \
        --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/cyfieithydd-7b-fersiwn-3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BangorAI/cyfieithydd-7b-fersiwn-3 with Docker Model Runner:
```
docker model run hf.co/BangorAI/cyfieithydd-7b-fersiwn-3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Cyfieithydd 7b Fersiwn 3

Model cyfieithu arbrofol sy'n defnyddio LLM Trawsnewidiol.

Mae'r model LLM yn seiliedig ar BangorAI/mistral-7b-cy-epoch-2, sef y model Mistral-7B wedi hyfforddiant parhaus ar gyfer y Gymraeg.

Cafodd y model hyfforddiant cywrain pellach ar y cyfeithiadau canlynol:

data Cofnod y Cynulliad wedi’u paratoi gan TechIaith
data Hysbysiadau Llywodraeth Cymru yn archif BangorAI/hysbysiadau-llyw-cymru-1

Demo

Gallwch roi gynnig ar esiampl o'r model yma: https://demo.bangor.ai/
(cofiwch ddewis yr opsiwn "Trosi En -> Cy")

Fformat Sgwrs

Mae'r hyfforddiant cywrain wedi defnyddio'r fformat canlynol ar gyfer trosi o'r Saesneg i'r Gymraeg:

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

### Cymraeg:

a'r naill ffordd i'r llall:

Translate the following Welsh text to English.
### Welsh:
{prompt}

### English:

Sut i'w ddefnyddio

Mae'r model Mistral-7B-v-0.1 sy'n tanseilio'r model yma yn defnyddio ffenestr cyd-destun maint 4k, felly mae'n ofyniad i becynnu'r testyn fesul paragraff a'u bwydo i mewn i'r LLM yn eu tro.

Dyma enghraifft mewn Python:

import transformers

device="cuda"
model_name = 'BangorAI/cyfieithydd-7b-fersiwn-3'
model = transformers.AutoModelForCausalLM.from_pretrained(model_name, load_in_8bit=True)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)

def generate_response(text):
    # Format the input using the provided template
    prompt = f"Cyfieithwch y testun Saesneg canlynol i'r Gymraeg.\n\n### Saesneg:\n{text}\n\n### Cymraeg:\n"
    # Tokenize and encode the prompt
    inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=False).to(device)
    # Generate a response
    outputs = model.generate(inputs, max_length=1000, num_return_sequences=1, do_sample=True, temperature=0.01)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response

text="""The extension of the Flying Start programme is part of a phased expansion of early years provision to all two-year-olds in Wales, with a particular emphasis on strengthening Welsh-medium provision. This is a commitment in the Co-operation Agreement between the Welsh Government and Plaid Cymru."""
response = generate_response(text)
print(response)

Hawlfraint

Mae'r data Cofnod y Cynulliad ac Hysbysiadau Llywodreath Cymru dan Drwydded Llywodraeth Agored.

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