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 / stack /training /train_config_local.yaml

walidsobhie-code

fix: add train_dir and eval_dir to config for Kaggle

24de6c8 about 1 month ago

raw

history blame contribute delete

1.1 kB

	model:
	name: /kaggle/working/stack-2.9/base_model_qwen7b
	trust_remote_code: true
	torch_dtype: float16

	data:
	input_path: /kaggle/working/stack-2.9/data/final/train.jsonl
	train_dir: null
	eval_dir: null
	max_length: 2048
	train_split: 0.9
	test_split: 0.1

	lora:
	r: 16
	alpha: 32
	dropout: 0.05
	target_modules:
	- q_proj
	- k_proj
	- v_proj
	- o_proj
	bias: none
	task_type: CAUSAL_LM

	training:
	num_epochs: 1
	batch_size: 2
	gradient_accumulation: 4
	learning_rate: 0.0002
	warmup_steps: 50
	weight_decay: 0.01
	max_grad_norm: 1.0
	logging_steps: 5
	eval_steps: 100
	save_steps: 200
	save_total_limit: 2
	fp16: true
	bf16: false
	gradient_checkpointing: true

	output:
	lora_dir: /kaggle/working/stack-2.9/training_output/lora
	merged_dir: /kaggle/working/stack-2.9/training_output/merged
	awq_dir: /kaggle/working/stack-2.9/training_output/awq

	quantization:
	enabled: false
	bits: 4
	group_size: 128

	logging:
	report_to: none
	wandb_project: stack-2.9-training

	hardware:
	device: cuda
	num_gpus: 1
	use_4bit: false
	use_8bit: false