Instructions to use moinsaj/aaie-8k-gft-5m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moinsaj/aaie-8k-gft-5m with Transformers:

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

pipe = pipeline("text-generation", model="moinsaj/aaie-8k-gft-5m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("moinsaj/aaie-8k-gft-5m")
model = AutoModelForCausalLM.from_pretrained("moinsaj/aaie-8k-gft-5m", 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 moinsaj/aaie-8k-gft-5m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moinsaj/aaie-8k-gft-5m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moinsaj/aaie-8k-gft-5m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/moinsaj/aaie-8k-gft-5m

SGLang

How to use moinsaj/aaie-8k-gft-5m 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 "moinsaj/aaie-8k-gft-5m" \
    --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": "moinsaj/aaie-8k-gft-5m",
		"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 "moinsaj/aaie-8k-gft-5m" \
        --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": "moinsaj/aaie-8k-gft-5m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use moinsaj/aaie-8k-gft-5m with Docker Model Runner:
```
docker model run hf.co/moinsaj/aaie-8k-gft-5m
```

AAIE 8K GFT — 5M checkpoint

This is the selected 5-million-token general-instruction-tuning checkpoint from the AAIE research project. It is a standalone, full-weight Hugging Face model: there is no LoRA adapter to attach or merge.

Lineage

AAIE 50M YaRN 8K base -> selective SmolTalk V2 general instruction tuning -> selected 5M checkpoint

Architecture: Llama-compatible dense decoder, 20 layers, hidden size 512, eight attention heads / two KV heads, tied embeddings.
Context configuration: 8,192 tokens using YaRN (factor: 8, original position length 1,024).
Tokenizer vocabulary: 151,936 tokens.
Training: full-weight, completion-masked instruction tuning; selected after a bounded 5M-token schedule at learning rate 1e-4.
Model-weight SHA-256: 4b1c4cc74f0f0dadd9923b4381151444c04e00682a39301b812a2116ec7c22b7.

The generation configuration corrects the checkpoint package's end-token settings: <|im_end|> (151645) and <|endoftext|> (151643) both stop generation.

Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "moinsaj/aaie-8k-gft-5m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [{"role": "user", "content": "Explain database indexes in two sentences."}]
inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
)
output = model.generate(inputs, max_new_tokens=160, do_sample=False)
print(tokenizer.decode(output[0, inputs.shape[-1]:], skip_special_tokens=True))

Important limitations

This is an AI-reviewed research checkpoint, not an educational assessment tool or a replacement for the AAIE product's hosted model. Its bounded evaluation showed better stopping behaviour than the unchanged base, but it did not demonstrate dependable instruction correctness, extraction, or criterion-level feedback. It requires further human/educator validation before any applied use.

The 8K context setting is an experimental YaRN extension. It should not be read as evidence of reliable general long-context reasoning.

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Safetensors

Model size

0.4B params

Tensor type

BF16