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: 3,291 Bytes
faf9686 a9b0b85 faf9686 a9b0b85 faf9686 a9b0b85 faf9686 a9b0b85 faf9686 a9b0b85 faf9686 a9b0b85 faf9686 a9b0b85 faf9686 a9b0b85 faf9686 | 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 | """
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}) |