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

Libraries

How to use thiomajid/hausa_blend with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-135M")
model = PeftModel.from_pretrained(base_model, "thiomajid/hausa_blend")

Transformers

How to use thiomajid/hausa_blend with Transformers:

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

pipe = pipeline("text-generation", model="thiomajid/hausa_blend")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("thiomajid/hausa_blend", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use thiomajid/hausa_blend with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/thiomajid/hausa_blend

SGLang

How to use thiomajid/hausa_blend 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 "thiomajid/hausa_blend" \
    --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": "thiomajid/hausa_blend",
		"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 "thiomajid/hausa_blend" \
        --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": "thiomajid/hausa_blend",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

hausa_blend

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M on the None dataset. It achieves the following results on the evaluation set:

Loss: 9.6934

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.002
train_batch_size: 64
eval_batch_size: 64
seed: 1652
gradient_accumulation_steps: 5
total_train_batch_size: 320
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.2
num_epochs: 6
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
213.2576	1.0	201	22.5111
61.0713	2.0	402	11.1953
53.5773	3.0	603	10.4559
51.1444	4.0	804	10.0289
49.6945	5.0	1005	9.7750
48.9875	6.0	1206	9.6934

Framework versions

PEFT 0.16.0
Transformers 4.54.1
Pytorch 2.7.1+cu126
Datasets 4.0.0
Tokenizers 0.21.2

Downloads last month: 1

Model tree for thiomajid/hausa_blend

Base model

HuggingFaceTB/SmolLM2-135M

Adapter

(22)

this model