Instructions to use Hishambarakat/Bahraini_Dialect_LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Hishambarakat/Bahraini_Dialect_LLM with PEFT:
```
Task type is invalid.
```

How to use Hishambarakat/Bahraini_Dialect_LLM with Transformers:

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

pipe = pipeline("text-generation", model="Hishambarakat/Bahraini_Dialect_LLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hishambarakat/Bahraini_Dialect_LLM")
model = AutoModelForCausalLM.from_pretrained("Hishambarakat/Bahraini_Dialect_LLM", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hishambarakat/Bahraini_Dialect_LLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hishambarakat/Bahraini_Dialect_LLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hishambarakat/Bahraini_Dialect_LLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hishambarakat/Bahraini_Dialect_LLM

SGLang

How to use Hishambarakat/Bahraini_Dialect_LLM 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 "Hishambarakat/Bahraini_Dialect_LLM" \
    --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": "Hishambarakat/Bahraini_Dialect_LLM",
		"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 "Hishambarakat/Bahraini_Dialect_LLM" \
        --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": "Hishambarakat/Bahraini_Dialect_LLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Hishambarakat/Bahraini_Dialect_LLM with Docker Model Runner:
```
docker model run hf.co/Hishambarakat/Bahraini_Dialect_LLM
```

Hishambarakat commited on Feb 21

Commit

4ee2864

verified ·

1 Parent(s): c5200f2

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@@ -85,7 +85,7 @@ tok = AutoTokenizer.from_pretrained(REPO_ID, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(REPO_ID, trust_remote_code=True, torch_dtype=DTYPE, device_map="auto")
 model.eval()
-SYSTEM = "تكلم بحريني طبيعي. تجنب الفصحى و(تمام/أرجو/عادة). استخدم: وايد، جذي، هني، شلون، عقبها/بعدها، ما ضبط. افترض مخاطب ذكر إلا إذا في مؤشرات أنثى."
 messages = [
   {"role":"system","content":SYSTEM},

 model = AutoModelForCausalLM.from_pretrained(REPO_ID, trust_remote_code=True, torch_dtype=DTYPE, device_map="auto")
 model.eval()
+SYSTEM = "أنت مساعد يتكلم باللهجة البحرينية بشكل طبيعي. خلك مختصر وعملي، وتجنب الفصحى واللغة الرسمية إلا إذا المستخدم طلب. إذا السؤال يحتاج توضيح عشان تجاوب صح، اسأل سؤال واحد أو اثنين بالكثير. حاول تخلي الأسلوب بحريني مو خليجي عام. افترض المخاطب ذكر إلا إذا واضح من كلام المستخدم غير جذي."
 messages = [
   {"role":"system","content":SYSTEM},