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 / MLproject

walidsobhie-code

feat: add production infrastructure - CI/CD, Docker, code quality, and monitoring

b5998ff about 2 months ago

raw

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1.74 kB

	name: stack-2.9

	python_env: python_env.yaml

	entry_points:
	main:
	command: "python train.py --train_data data/final/train.jsonl --val_data data/final/val.jsonl"

	evaluate:
	command: "python evaluate_model.py --model models/checkpoint --eval_data data/final/test.jsonl"

	augment:
	command: "python scripts/augment_training_data.py --input training-data/tool_examples.jsonl --output training-data/augmented.jsonl --multiplier 3"

	validate:
	command: "python scripts/validate_training_data.py --input training-data/tool_examples.jsonl"

	parameters:
	- name: train_data
	default: data/final/train.jsonl
	- name: val_data
	default: data/final/val.jsonl
	- name: model_name
	default: Qwen/Qwen2.5-7B
	- name: batch_size
	default: 4
	type: int
	- name: learning_rate
	default: 5.0e-5
	type: float
	- name: num_epochs
	default: 3
	type: int
	- name: warmup_steps
	default: 100
	type: int
	- name: max_seq_length
	default: 8192
	type: int
	- name: gradient_accumulation_steps
	default: 4
	type: int
	- name: lora_rank
	default: 16
	type: int
	- name: lora_alpha
	default: 32
	type: int
	- name: lora_dropout
	default: 0.05
	type: float
	- name: use_flash_attention
	default: true
	type: bool

	run_options:
	# Storage for MLflow tracking
	tracking_uri: ./mlruns

	# Experiment configuration
	experiment:
	name: stack-2.9-training
	description: "Stack 2.9 model training experiments"

	# Resource limits
	resources:
	gpu_count: 1
	gpu_type: A100

	# Logging configuration
	log_model:
	artifacts: true
	save_steps: 500

	# Early stopping
	early_stopping:
	metric: eval_loss
	patience: 2
	min_delta: 0.001