Instructions to use Joshua-Abok/Malawi-Public-Health-Systems with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Joshua-Abok/Malawi-Public-Health-Systems with Transformers:

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

pipe = pipeline("text-generation", model="Joshua-Abok/Malawi-Public-Health-Systems")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Joshua-Abok/Malawi-Public-Health-Systems")
model = AutoModelForCausalLM.from_pretrained("Joshua-Abok/Malawi-Public-Health-Systems")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Joshua-Abok/Malawi-Public-Health-Systems with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Joshua-Abok/Malawi-Public-Health-Systems

SGLang

How to use Joshua-Abok/Malawi-Public-Health-Systems 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 "Joshua-Abok/Malawi-Public-Health-Systems" \
    --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": "Joshua-Abok/Malawi-Public-Health-Systems",
		"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 "Joshua-Abok/Malawi-Public-Health-Systems" \
        --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": "Joshua-Abok/Malawi-Public-Health-Systems",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Joshua-Abok/Malawi-Public-Health-Systems with Docker Model Runner:
```
docker model run hf.co/Joshua-Abok/Malawi-Public-Health-Systems
```

Malawi-Public-Health-Systems

This model is a fine-tuned version of EleutherAI/pythia-410m on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.9453

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: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 400
total_train_batch_size: 400
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss
0.1272	71.32	120	3.2478
0.1003	142.64	240	3.6082
0.1017	213.97	360	3.7057
0.1001	285.29	480	3.7969
0.0949	356.61	600	3.8484
0.0984	427.93	720	3.8783
0.0956	499.26	840	3.9172
0.0955	570.58	960	3.9453

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2

Downloads last month: 5

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for Joshua-Abok/Malawi-Public-Health-Systems

Base model

EleutherAI/pythia-410m

Finetuned

(219)

this model