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

File size: 4,170 Bytes

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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
1. **Preprocessing**: Standardized test set preparation with sanitization
2. **Inference**: Automated generation of responses for each test case
3. **Verification**: Automated test execution for coding problems
4. **Analysis**: Statistical aggregation and result compilation
5. **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:

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

---

**Stack 2.9 Benchmark Status**: In Progress | Results Coming Soon