Text Generation
Transformers
English
qwen2
code-generation
python
fine-tuning
Qwen
tools
agent-framework
multi-agent
conversational
Eval Results (legacy)
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
- Transformers
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", device_map="auto") 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
File size: 3,065 Bytes
b6ae7b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | 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
|