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
```

walidsobhie-code Claude Opus 4.6 commited on Apr 4

Commit

9ce0c00

1 Parent(s): 30d572f

feat: combine RTMP tools with main training data

Browse files

- train_with_rtmp.jsonl: 41,866 examples
- Includes RTMP tool patterns (59 examples)
- Config updated to use combined dataset

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Files changed (1) hide show

stack/training/train_config_local.yaml +6 -6

stack/training/train_config_local.yaml CHANGED Viewed

@@ -8,9 +8,9 @@ model:
 # Data Configuration
 data:
-  input_path: "/Users/walidsobhi/.openclaw/workspace/stack-2.9/training-data/generated/synthetic_50k.jsonl"
-  train_dir: "/Users/walidsobhi/.openclaw/workspace/stack-2.9-training/data/train"
-  eval_dir: "/Users/walidsobhi/.openclaw/workspace/stack-2.9-training/data/eval"
   max_length: 2048
   train_split: 0.9
   test_split: 0.1
@@ -47,9 +47,9 @@ training:
 # Output Configuration
 output:
-  lora_dir: "/Users/walidsobhi/.openclaw/workspace/stack-2.9-training/output/stack-2.9-7b-lora"
-  merged_dir: "/Users/walidsobhi/.openclaw/workspace/stack-2.9-training/output/stack-2.9-7b-merged"
-  awq_dir: "/Users/walidsobhi/.openclaw/workspace/stack-2.9-training/output/stack-2.9-7b-awq"
 # Quantization Configuration
 quantization:

 # Data Configuration
 data:
+  input_path: "./data/final/train_with_rtmp.jsonl"
+  train_dir: null
+  eval_dir: null
   max_length: 2048
   train_split: 0.9
   test_split: 0.1
 # Output Configuration
 output:
+  lora_dir: "./training_output/lora"
+  merged_dir: "./training_output/merged"
+  awq_dir: "./training_output/awq"
 # Quantization Configuration
 quantization: