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 / docs /MODEL_REGISTRY.md

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	# 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 \|

	# 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 \|