Instructions to use tasal9/ZamAI-Mistral-7B-Pashto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tasal9/ZamAI-Mistral-7B-Pashto with Transformers:

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

pipe = pipeline("text-generation", model="tasal9/ZamAI-Mistral-7B-Pashto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tasal9/ZamAI-Mistral-7B-Pashto")
model = AutoModelForCausalLM.from_pretrained("tasal9/ZamAI-Mistral-7B-Pashto")
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

vLLM

How to use tasal9/ZamAI-Mistral-7B-Pashto with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tasal9/ZamAI-Mistral-7B-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-Mistral-7B-Pashto",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tasal9/ZamAI-Mistral-7B-Pashto

SGLang

How to use tasal9/ZamAI-Mistral-7B-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-Mistral-7B-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-Mistral-7B-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-Mistral-7B-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-Mistral-7B-Pashto",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tasal9/ZamAI-Mistral-7B-Pashto with Docker Model Runner:
```
docker model run hf.co/tasal9/ZamAI-Mistral-7B-Pashto
```

ZamAI-Mistral-7B-Pashto

Fine-tuned Mistral-7B for educational tutoring with Pashto language support

🌟 Model Overview

This model is part of the ZamAI Pro Models Strategy - a comprehensive AI platform designed for multilingual applications with specialized focus on Pashto language support.

Key Features

🧠 Advanced AI: Based on mistralai/Mistral-7B-Instruct-v0.1 architecture
🌐 Multilingual: Optimized for Pashto and English
⚡ High Performance: Optimized for production deployment
🔒 Secure: Enterprise-grade security and privacy

📚 Usage

Basic Usage with Transformers

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("tasal9/ZamAI-Mistral-7B-Pashto")
model = AutoModel.from_pretrained("tasal9/ZamAI-Mistral-7B-Pashto")

# Example usage
text = "Your input text here"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

Usage with Hugging Face Inference API

from huggingface_hub import InferenceClient

client = InferenceClient(token="your_hf_token")

response = client.text_generation(
    model="tasal9/ZamAI-Mistral-7B-Pashto",
    prompt="Your prompt here",
    max_new_tokens=200
)

🔧 Technical Details

Model Type: text-generation
Base Model: mistralai/Mistral-7B-Instruct-v0.1
Languages: Pashto (ps), English (en)
License: MIT
Training: Fine-tuned on Pashto educational and cultural content

🚀 Applications

This model powers:

ZamAI Educational Platform: Pashto language tutoring
Business Automation: Document processing and analysis
Voice Assistants: Natural language understanding
Cultural Preservation: Supporting Pashto language technology

📞 Support

For support and integration assistance:

📧 Email: support@zamai.ai
🌐 Website: zamai.ai
💬 Community: ZamAI Community

📄 License

Licensed under the MIT License.

Part of the ZamAI Pro Models Strategy - Transforming AI for Multilingual Applications 🌟

Updated: 2025-07-05 21:29:09 UTC

Downloads last month: 2

Model tree for tasal9/ZamAI-Mistral-7B-Pashto

Base model

mistralai/Mistral-7B-v0.1

Finetuned

mistralai/Mistral-7B-Instruct-v0.1