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") 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 - Web UI Chat | |
| Simple web interface using Streamlit | |
| """ | |
| import streamlit as st | |
| import os | |
| import requests | |
| import json | |
| # Configure page | |
| st.set_page_config( | |
| page_title="Stack 2.9", | |
| page_icon="💻", | |
| layout="wide" | |
| ) | |
| # Title | |
| st.title("💻 Stack 2.9") | |
| st.caption("AI Coding Assistant") | |
| # Sidebar settings | |
| with st.sidebar: | |
| st.header("Settings") | |
| model = st.selectbox( | |
| "Model", | |
| ["minimax-m2.5:cloud", "qwen2.5-coder:1.5b"], | |
| index=0 | |
| ) | |
| temperature = st.slider("Temperature", 0.0, 2.0, 0.7, 0.1) | |
| max_tokens = st.slider("Max Tokens", 100, 4096, 2048, 100) | |
| st.divider() | |
| if st.button("Clear Chat"): | |
| st.session_state.messages = [] | |
| st.rerun() | |
| # Initialize chat history | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [ | |
| {"role": "assistant", "content": "Hello! I'm Stack 2.9, your AI coding assistant. How can I help?"} | |
| ] | |
| # Display chat messages | |
| for msg in st.session_state.messages: | |
| with st.chat_message(msg["role"]): | |
| st.markdown(msg["content"]) | |
| # Chat input | |
| if prompt := st.chat_input("Type your message..."): | |
| # Add user message | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| # Show user message | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # Generate response | |
| with st.chat_message("assistant"): | |
| with st.spinner("Thinking..."): | |
| try: | |
| import json | |
| # Use local Ollama - your minimax is registered there | |
| response = requests.post( | |
| "http://localhost:11434/api/chat", | |
| json={ | |
| "model": model, | |
| "messages": [ | |
| {"role": m["role"], "content": m["content"]} | |
| for m in st.session_state.messages | |
| ], | |
| "temperature": temperature, | |
| "max_tokens": max_tokens | |
| }, | |
| timeout=120, | |
| stream=False | |
| ) | |
| if response.status_code == 200: | |
| text = response.text.strip() | |
| # Try to parse each line until we get content | |
| assistant_msg = "" | |
| for line in text.split('\n'): | |
| if line.strip(): | |
| try: | |
| result = json.loads(line) | |
| content = result.get("message", {}).get("content", "") | |
| if content: | |
| assistant_msg = content | |
| break | |
| except: | |
| continue | |
| if not assistant_msg: | |
| assistant_msg = text | |
| else: | |
| assistant_msg = f"Error: {response.status_code}\n{response.text[:200]}" | |
| except Exception as e: | |
| assistant_msg = f"Connection Error: {str(e)}\n\nMake sure Ollama is running with: ollama serve" | |
| st.markdown(assistant_msg) | |
| st.session_state.messages.append({"role": "assistant", "content": assistant_msg}) |