Instructions to use tasal9/ZamAI-LIama3-Pashto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tasal9/ZamAI-LIama3-Pashto with Transformers:

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

pipe = pipeline("text-generation", model="tasal9/ZamAI-LIama3-Pashto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tasal9/ZamAI-LIama3-Pashto")
model = AutoModelForCausalLM.from_pretrained("tasal9/ZamAI-LIama3-Pashto", 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 tasal9/ZamAI-LIama3-Pashto with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tasal9/ZamAI-LIama3-Pashto"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tasal9/ZamAI-LIama3-Pashto",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tasal9/ZamAI-LIama3-Pashto

SGLang

How to use tasal9/ZamAI-LIama3-Pashto 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 "tasal9/ZamAI-LIama3-Pashto" \
    --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": "tasal9/ZamAI-LIama3-Pashto",
		"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 "tasal9/ZamAI-LIama3-Pashto" \
        --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": "tasal9/ZamAI-LIama3-Pashto",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tasal9/ZamAI-LIama3-Pashto with Docker Model Runner:
```
docker model run hf.co/tasal9/ZamAI-LIama3-Pashto
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ZamAI LIama3 Pashto

Task: text-generation
Languages: en, ps
Base model: meta-llama/Meta-Llama-3-8B-Instruct
Training dataset(s): tasal9/Pashto-Dataset-Creating-Dataset

Overview

This model is part of the ZamAI Pashto language AI collection. It is fine-tuned/adapted for text-generation in Pashto and related languages.

Intended uses & limitations

Use for research, prototyping, and production assistance in Pashto NLP.
Evaluate outputs carefully before deploying in high-stakes applications.
May reflect biases present in the pre-training or fine-tuning data.

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tasal9/ZamAI-LIama3-Pashto"
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

prompt = "سلام دې وي! تاسو څنګه یاست؟"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training data

The model was trained or fine-tuned on: tasal9/Pashto-Dataset-Creating-Dataset.

Evaluation

Metric	Value	Description
TBD	TBD	Add measured value

Update this table with your measured results and link to the evaluation script/notebook.

Citation

@misc{zamai_pashto,
  title = {{ZamAI LIama3 Pashto}},
  author = {ZamAI / Yaqoob Tasal},
  year = {2024},
  howpublished = {\url{https://huggingface.co/tasal9/ZamAI-LIama3-Pashto}}
}

License

This model is released under the mit license.

Downloads last month: 291

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for tasal9/ZamAI-LIama3-Pashto

Base model

meta-llama/Meta-Llama-3-8B-Instruct

Finetuned

(1086)

this model

Quantizations

1 model