Instructions to use tokenaii/Horus-Taleeq-0.2B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tokenaii/Horus-Taleeq-0.2B-Base with Transformers:

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

pipe = pipeline("text-generation", model="tokenaii/Horus-Taleeq-0.2B-Base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tokenaii/Horus-Taleeq-0.2B-Base")
model = AutoModelForCausalLM.from_pretrained("tokenaii/Horus-Taleeq-0.2B-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tokenaii/Horus-Taleeq-0.2B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tokenaii/Horus-Taleeq-0.2B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tokenaii/Horus-Taleeq-0.2B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tokenaii/Horus-Taleeq-0.2B-Base

SGLang

How to use tokenaii/Horus-Taleeq-0.2B-Base 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 "tokenaii/Horus-Taleeq-0.2B-Base" \
    --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": "tokenaii/Horus-Taleeq-0.2B-Base",
		"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 "tokenaii/Horus-Taleeq-0.2B-Base" \
        --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": "tokenaii/Horus-Taleeq-0.2B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tokenaii/Horus-Taleeq-0.2B-Base with Docker Model Runner:
```
docker model run hf.co/tokenaii/Horus-Taleeq-0.2B-Base
```

Fix param count: 199,916,160 (≈200M), not 204.6M

by Compactbot - opened 20 days ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-1

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -39,7 +39,7 @@ base checkpoint, not a finished instruction-tuned or production chat release.
 | --- | --- |
 | Model family | Horus Taleeq |
 | Model type | Arabic-only Small Language Model (SLM), decoder-only causal Transformer |
-| Approximate parameters | 0.2B (about 204.6M) |
 | Primary language | Arabic |
 | Intended text | Modern Standard Arabic and Arabic dialectal text |
 | Egyptian Arabic | Targeted in the training program; not independently certified by this base checkpoint |

 | --- | --- |
 | Model family | Horus Taleeq |
 | Model type | Arabic-only Small Language Model (SLM), decoder-only causal Transformer |
+| Approximate parameters | 0.2B (199,916,160 ≈ 200M) |
 | Primary language | Arabic |
 | Intended text | Modern Standard Arabic and Arabic dialectal text |
 | Egyptian Arabic | Targeted in the training program; not independently certified by this base checkpoint |