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 Settings

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 /deploy /kubernetes /deployment.yaml

walidsobhie-code

refactor: Squeeze folders further - cleaner structure

65888d5 2 months ago

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	apiVersion: apps/v1
	kind: Deployment
	metadata:
	name: stack-2.9
	namespace: stack-2.9
	labels:
	app: stack-2.9
	version: "2.9"
	spec:
	replicas: 1
	selector:
	matchLabels:
	app: stack-2.9
	template:
	metadata:
	labels:
	app: stack-2.9
	version: "2.9"
	spec:
	containers:
	- name: stack-2.9
	image: your-registry/stack-2.9:latest
	imagePullPolicy: IfNotPresent
	ports:
	- containerPort: 8000
	name: http
	protocol: TCP
	env:
	- name: MODEL_ID
	value: "TheBloke/Llama-2-7B-Chat-AWQ"
	- name: HUGGING_FACE_TOKEN
	valueFrom:
	secretKeyRef:
	name: stack-2.9-secrets
	key: huggingface-token
	- name: QUANTIZATION
	value: "awq"
	- name: TENSOR_PARALLEL_SIZE
	value: "1"
	- name: GPU_MEMORY_UTILIZATION
	value: "0.9"
	- name: MAX_MODEL_LEN
	value: "4096"
	- name: MAX_NUM_SEQS
	value: "64"
	- name: MAX_NUM_BATCHED_TOKENS
	value: "4096"
	- name: ENFORCE_EAGER
	value: "false"
	- name: DISABLE_LOG_STATS
	value: "false"
	- name: HOST
	value: "0.0.0.0"
	- name: PORT
	value: "8000"
	- name: MODEL_CACHE_DIR
	value: "/models"
	- name: OMP_NUM_THREADS
	value: "4"
	resources:
	limits:
	nvidia.com/gpu: 1
	memory: "16Gi"
	cpu: "4"
	requests:
	nvidia.com/gpu: 1
	memory: "8Gi"
	cpu: "2"
	volumeMounts:
	- name: model-cache
	mountPath: /models
	livenessProbe:
	httpGet:
	path: /health
	port: 8000
	initialDelaySeconds: 60
	periodSeconds: 30
	timeoutSeconds: 10
	failureThreshold: 3
	readinessProbe:
	httpGet:
	path: /health
	port: 8000
	initialDelaySeconds: 30
	periodSeconds: 10
	timeoutSeconds: 5
	failureThreshold: 3
	securityContext:
	allowPrivilegeEscalation: false
	runAsNonRoot: true
	runAsUser: 1000
	capabilities:
	drop:
	- ALL
	volumes:
	- name: model-cache
	persistentVolumeClaim:
	claimName: stack-2.9-model-cache
	nodeSelector:
	# Uncomment to schedule on GPU nodes only
	# nvidia.com/gpu.product: A100-80GB
	accelerator: nvidia-tesla
	tolerations:
	- key: "nvidia.com/gpu"
	operator: "Exists"
	effect: "NoSchedule"
	affinity:
	podAntiAffinity:
	preferredDuringSchedulingIgnoredDuringExecution:
	- weight: 100
	podAffinityTerm:
	labelSelector:
	matchExpressions:
	- key: app
	operator: In
	values:
	- stack-2.9
	topologyKey: kubernetes.io/hostname