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
```

Walid Sobhi commited on Apr 12

Commit

b7fe8b2

verified ·

1 Parent(s): af5d014

Update Model Card with audited technical specs and agent features

Browse files

Files changed (1) hide show

README.md +15 -2

README.md CHANGED Viewed

@@ -86,10 +86,24 @@ Stack 2.9 is a code generation model fine-tuned from Qwen2.5-Coder-1.5B, paired
 ---
 ## 🚀 Quick Start
 ### 1. Load the Model
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -102,7 +116,6 @@ tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned")
 ```
 ### 2. Use the Tool Framework
 ```python
 from src.tools import get_registry

 ---
+## 🧠 Advanced Intelligence Enhancements
+Stack 2.9 is more than just a code generator; it is an intelligent agent equipped with a suite of cognitive enhancements:
+| Enhancement | Capability | Technical Implementation |
+| :--- | :--- | :--- |
+| **Emotional Intelligence** | Real-time sentiment detection and empathetic response adjustment | Hybrid Transformer-based (`distilbert`) + rule-based engine |
+| **Knowledge Graph** | Structured relationship mapping and high-precision context retrieval | `networkx` MultiDiGraph with RAG integration |
+| **Advanced NLP** | Precise intent detection and hybrid Named Entity Recognition (NER) | BERT-based NER + pattern-matching intent classifier |
+| **Technical Suite** | Automated static analysis, complexity auditing, and error mapping | Cyclomatic complexity analysis & traceback-to-cause mapping |
+| **Learning Loop** | Continuous improvement via user feedback and performance telemetry | Feedback collection system for iterative fine-tuning |
+| **Collaboration** | Model Context Protocol (MCP) for real-time environment interaction | MCP Client/Server implementation for tool standardization |
+---
 ## 🚀 Quick Start
 ### 1. Load the Model
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 ```
 ### 2. Use the Tool Framework
 ```python
 from src.tools import get_registry