Instructions to use SaidzodaEng/Ameena_Qwen3-8B_e3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SaidzodaEng/Ameena_Qwen3-8B_e3 with Transformers:

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

pipe = pipeline("text-generation", model="SaidzodaEng/Ameena_Qwen3-8B_e3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("SaidzodaEng/Ameena_Qwen3-8B_e3")
model = AutoModelForCausalLM.from_pretrained("SaidzodaEng/Ameena_Qwen3-8B_e3", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SaidzodaEng/Ameena_Qwen3-8B_e3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SaidzodaEng/Ameena_Qwen3-8B_e3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SaidzodaEng/Ameena_Qwen3-8B_e3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SaidzodaEng/Ameena_Qwen3-8B_e3

SGLang

How to use SaidzodaEng/Ameena_Qwen3-8B_e3 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 "SaidzodaEng/Ameena_Qwen3-8B_e3" \
    --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": "SaidzodaEng/Ameena_Qwen3-8B_e3",
		"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 "SaidzodaEng/Ameena_Qwen3-8B_e3" \
        --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": "SaidzodaEng/Ameena_Qwen3-8B_e3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SaidzodaEng/Ameena_Qwen3-8B_e3 with Docker Model Runner:
```
docker model run hf.co/SaidzodaEng/Ameena_Qwen3-8B_e3
```

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Ameena-Qwen3-8B-Tajik-v3

A fully fine-tuned Qwen3-8B model trained on 10B+ tokens of native Tajik, Uzbek, and Russian educational content over 3 epochs. Powers the core AI tutor in Ameena.tj — Central Asia’s first sovereign learning platform.

Model Details

Developed by: Saidzoda AI Research Lab (IT Park Tajikistan)
Base model: Qwen/Qwen3-8B
Training data: 10B+ tokens from textbooks, exams, literature, and technical manuals
Languages: Tajik (primary), Uzbek, Russian, English
Epochs: 3
License: Apache 2.0

Use Case

AI-powered course generation
Step-by-step tutoring (explains, doesn’t solve)
Homework analysis & certification
Offline inference on consumer devices

Training Infrastructure

Hardware: Nvidia H200
Framework: Hugging Face Transformers + Accelerate
Precision: BF16 mixed precision
Duration: ~220 hours

Environmental Impact

Cloud Provider: Runpod
Region: europe-west4
Carbon Emitted: ~2.2 kg CO₂eq
(Estimated via ML CO2 Impact Calculator)

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "SaidzodaEng/Ameena_Qwen3-8B_e3"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

Out-of-Scope Use

Not for medical, legal, or financial advice
Not intended for high-stakes decision-making
Citation

APA:
Saidzoda AI Research Lab. (2025). Ameena_Qwen3-8B_e3. Hugging Face.

BibTeX

@misc{saidzoda_ameena_qwen3_2025,
  author = {Saidzoda AI Research Lab},
  title = {Ameena-Qwen3-8B-Tajik-v3},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/SaidzodaEng/Ameena_Qwen3-8B_e3}}
}

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Safetensors

Model size

8B params

Tensor type

BF16

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