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

62e8962

verified ·

1 Parent(s): 98fe18d

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README.md +32 -21

README.md CHANGED Viewed

@@ -122,7 +122,7 @@ The dataset was produced through a structured pipeline:
 * Controlled synthetic generation to expand coverage while keeping the same voice
 * A dialect rule-set (positive/negative constraints) to:
-  * encourage Bahraini lexical markers (e.g., وايد، جذي، هني، شلون، عقبها/بعدها، ما ضبط)
   * discourage MSA scaffolding and overly formal connectors
   * keep responses short and practical
 * Template correctness via the ALLaM chat template, with EOS enforcement
@@ -146,26 +146,37 @@ Data was formatted using ALLaM’s chat template:
 Base configuration used during the run:
-* **Max sequence length:** 2048
-* **Optimizer:** `adamw_torch`
-* **LR:** 2e-5
-* **Scheduler:** cosine
-* **Warmup:** 0.1 of optimizer steps (computed as `warmup_steps`)
-* **Weight decay:** 0.01
-* **Max grad norm:** 1.0
-* **Batching:** `per_device_train_batch_size=4`, `gradient_accumulation_steps=16`
-* **Epochs:** 4
-* **Packing:** False
-* **Seed:** 42
-* **Precision:** fp16 on T4; bf16 on Ampere+
-* **Attention impl:** eager
-* **Gradient checkpointing:** enabled (`use_reentrant=False`)
-* **LoRA:**
-  * r=16
-  * alpha=32
-  * dropout=0.05
-  * target modules: `q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj`
 ### Notes on Tokenizer / Special Tokens

 * Controlled synthetic generation to expand coverage while keeping the same voice
 * A dialect rule-set (positive/negative constraints) to:
+  * encourage Bahraini lexical markers (e.g., وايد، جذي، هني، شلون، عقبها/بعدها)
   * discourage MSA scaffolding and overly formal connectors
   * keep responses short and practical
 * Template correctness via the ALLaM chat template, with EOS enforcement
 Base configuration used during the run:
+```yaml
+max_seq_length: 2048
+optimizer: adamw_torch
+learning_rate: 2e-5
+lr_scheduler: cosine
+warmup_ratio: 0.1
+weight_decay: 0.01
+max_grad_norm: 1.0
+per_device_train_batch_size: 4
+gradient_accumulation_steps: 16
+num_train_epochs: 4
+packing: false
+seed: 42
+precision: fp16 (T4) / bf16 (Ampere+)
+attention_implementation: eager
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+lora:
+  r: 16
+  alpha: 32
+  dropout: 0.05
+  target_modules:
+    - q_proj
+    - k_proj
+    - v_proj
+    - o_proj
+    - gate_proj
+    - up_proj
+    - down_proj
+```
 ### Notes on Tokenizer / Special Tokens