Instructions to use my-ai-stack/Stack-2-9-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use my-ai-stack/Stack-2-9-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="my-ai-stack/Stack-2-9-finetuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
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 my-ai-stack/Stack-2-9-finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "my-ai-stack/Stack-2-9-finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/my-ai-stack/Stack-2-9-finetuned

SGLang

How to use my-ai-stack/Stack-2-9-finetuned 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 "my-ai-stack/Stack-2-9-finetuned" \
    --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": "my-ai-stack/Stack-2-9-finetuned",
		"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 "my-ai-stack/Stack-2-9-finetuned" \
        --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": "my-ai-stack/Stack-2-9-finetuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use my-ai-stack/Stack-2-9-finetuned with Docker Model Runner:
```
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
```

Stack-2-9-finetuned

File size: 2,128 Bytes

4ca507e

# Stack 2.9 Model Registry

> Version tracking for all Stack 2.9 model variants.

---

## Model Versions

| Version | Status | Date | Base Model | Parameters | Dataset | Performance | Use Case |
|---------|--------|------|------------|------------|---------|-------------|----------|
| `stack-2.9-1.5B` | 🟡 In Training | 2026-04-06 | Llama 3.2-1B | 1.5B | Stack 2.9 dedup | TBD | Research, fine-tuning base |
| `stack-2.9-7B` | 🔴 Planned | TBD | Llama 3.1-8B | 7B | Stack 2.9 dedup | TBD | General-purpose inference |
| `stack-2.9-7B-QLoRA` | 🔴 Planned | TBD | Llama 3.1-8B | 7B (quantized) | Stack 2.9 dedup | TBD | Edge deployment, low-memory |

---

## Version Details

### stack-2.9-1.5B (Current)

- **Status:** In Training
- **Architecture:** Transformer (pretrained)
- **Base Model:** Llama 3.2-1B
- **Parameters:** 1.5B
- **Training Data:** Stack 2.9 deduplicated
- **Context Length:** 128k tokens
- **Vocabulary Size:** ~128K
- **Precision:** BF16
- **Training Hardware:** 8x H100 (TBD确认)
- **Expected Completion:** TBD
- **Notes:** First iteration of Stack 2.9, used as baseline for larger variants

### stack-2.9-7B (Planned)

- **Status:** Planned
- **Architecture:** Transformer (pretrained)
- **Base Model:** Llama 3.1-8B
- **Parameters:** 7B
- **Training Data:** Stack 2.9 deduplicated
- **Context Length:** 128k tokens
- **Vocabulary Size:** ~128K
- **Precision:** BF16
- **Training Hardware:** TBD
- **Expected Start:** TBD
- **Notes:** Scale-up from 1.5B, targeting general-purpose use

### stack-2.9-7B-QLoRA (Planned)

- **Status:** Planned
- **Architecture:** Transformer + QLoRA
- **Base Model:** Llama 3.1-8B
- **Parameters:** 7B (4-bit quantized)
- **Training Data:** Stack 2.9 deduplicated
- **Context Length:** 128k tokens
- **Vocabulary Size:** ~128K
- **Quantization:** 4-bit NF4
- **LoRA Rank:** TBD
- **LoRA Alpha:** TBD
- **LoRA Dropout:** TBD
- **Target Modules:** TBD
- **Notes:** Quantized for consumer GPU deployment (e.g., 24GB VRAM)

---

## Changelog

| Date | Version | Change |
|------|---------|--------|
| 2026-04-06 | stack-2.9-1.5B | Initial entry — training started |