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
```

Stack-2-9-finetuned / stack /docs /reference /BENCHMARKS.md

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Benchmark Results - Stack 2.9

Note: These benchmarks are currently in progress. Results will be published after training is complete.

Benchmark Overview

Stack 2.9 will be evaluated on a comprehensive suite of benchmarks to measure coding capabilities, tool use proficiency, and overall model performance. The evaluation framework includes both standard coding benchmarks and custom tool-use scenarios.

Planned Benchmarks

1. HumanEval

Description: A set of 164 Python programming problems from OpenAI's HumanEval benchmark. Metrics: Pass@k (k=1, 10, 100) Expected Range: 70-80% pass@1 (based on Qwen2.5-Coder-32B baseline of ~76.8%) Status: Scheduled for post-training evaluation

2. MBPP (Mostly Basic Python Programming)

Description: 500 Python function synthesis problems from Google's MBPP dataset. Metrics: Pass@1, execution accuracy Expected Range: 80-85% pass@1 (based on Qwen2.5-Coder-32B baseline of ~82.3%) Status: Scheduled for post-training evaluation

3. SWE-bench

Description: Real-world GitHub issues requiring code modifications and debugging. This is the most challenging software engineering benchmark. Metrics: Resolution rate, edit similarity, test pass rate Expected Range: 15-25% resolution rate (based on similar 32B parameter models) Status: Planned for comprehensive testing post-training

4. Tool Use Accuracy (Custom OpenClaw Suite)

Description: 500 tasks covering OpenClaw-specific tool patterns: file operations, search, API calls, system commands, data processing, and multi-step workflows. Metrics: Task completion rate, tool call accuracy, parameter correctness, workflow success Expected Range: 85-92% overall task completion (conservative estimate based on fine-tuning for tool patterns) Status: Evaluation framework in development

Additional Evaluations

Context Understanding

Long-context benchmark: Testing 128K token window utilization
Multi-file reasoning: Cross-file code comprehension and modification

Specialized Domains

Voice Integration: Voice command processing and response generation
Documentation Generation: Quality assessment of auto-generated API docs
Code Review: Bug detection and suggestion quality

Results Template

Once evaluations are complete, results will be published in the following format:

Benchmark	Pass@1 / Score	Sample Size	Evaluation Date	Notes
HumanEval	TBD	164 problems	TBD	Standard Python coding
MBPP	TBD	500 problems	TBD	Basic Python synthesis
SWE-bench	TBD	Varies	TBD	Real-world GitHub issues
Tool Use	TBD	500 tasks	TBD	OpenClaw tool patterns
GSM8K	TBD	1319 problems	TBD	Math reasoning (optional)

Benchmark Methodology

Testing Conditions

Temperature: 0.2 (for code generation tasks)
Top_p: 0.95
Batch size: 1 (unless otherwise noted)
Hardware: NVIDIA A100 80GB (or equivalent)
Quantization: AWQ 4-bit where applicable
Inference engine: vLLM or similar for throughput testing

Evaluation Process

Preprocessing: Standardized test set preparation with sanitization
Inference: Automated generation of responses for each test case
Verification: Automated test execution for coding problems
Analysis: Statistical aggregation and result compilation
Documentation: Detailed methodology and raw results publication

Timeline

Training Completion: [Date to be announced]
Benchmark Execution: 1-2 weeks post-training
Results Analysis: 1 week
Public Release: 1 week after analysis completion

Publication

Results will be published in multiple formats:

This document (BENCHMARKS.md) - Summary tables and key findings
Detailed report ( BENCHMARKS_DETAILED.md) - In-depth methodology and raw scores
GitHub Release - Official results with reproducible evaluation scripts
OpenRouter listing - Performance metrics for model comparison

Stack 2.9 Benchmark Status: In Progress | Results Coming Soon