Instructions to use tensense/code_repo_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensense/code_repo_finetuning with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tensense/code_repo_finetuning")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensense/code_repo_finetuning", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tensense/code_repo_finetuning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensense/code_repo_finetuning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensense/code_repo_finetuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tensense/code_repo_finetuning

SGLang

How to use tensense/code_repo_finetuning 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 "tensense/code_repo_finetuning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensense/code_repo_finetuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "tensense/code_repo_finetuning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tensense/code_repo_finetuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tensense/code_repo_finetuning with Docker Model Runner:
```
docker model run hf.co/tensense/code_repo_finetuning
```

code_repo_finetuning / repos /Laddr /.github /pull_request_template.md

tensense

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# Pull Request for Laddr

Thank you for contributing to **Laddr**, the open-source multi-agent framework by **Agnet Labs**!  
Please fill out the sections below to help us review your PR quickly and effectively.

---

## Description
A clear and concise description of what this pull request does.

---

## Related Issue
Link to any related issue(s):  
Fixes #<issue_number>

---

## Testing
Steps to verify that your changes work as expected:
1. 
2. 
3. 

---

## Screenshots / Logs (optional)
Add any relevant screenshots, logs, or outputs that help explain your changes.

---

## Type of Change
Select the type(s) of change your PR introduces:

- [ ] Bug fix  
- [ ] New feature  
- [ ] Documentation update  
- [ ] Refactor / code cleanup  
- [ ] Build or config change  
- [ ] Other (please describe below)

---

## Checklist
Before submitting, please confirm that you have:

- [ ] Read and followed the [CONTRIBUTING.md](../CONTRIBUTING.md) guidelines  
- [ ] Tested the changes locally  
- [ ] Updated relevant documentation (if applicable)  
- [ ] Linked any related issues

---

### Additional Notes
Add any extra context or information about the PR here (optional).

---

**Thank you for contributing to Laddr! 🧠**

	# Pull Request for Laddr

	Thank you for contributing to Laddr, the open-source multi-agent framework by Agnet Labs!
	Please fill out the sections below to help us review your PR quickly and effectively.

	---

	## Description
	A clear and concise description of what this pull request does.

	---

	## Related Issue
	Link to any related issue(s):
	Fixes #<issue_number>

	---

	## Testing
	Steps to verify that your changes work as expected:
	1.
	2.
	3.

	---

	## Screenshots / Logs (optional)
	Add any relevant screenshots, logs, or outputs that help explain your changes.

	---

	## Type of Change
	Select the type(s) of change your PR introduces:

	- [ ] Bug fix
	- [ ] New feature
	- [ ] Documentation update
	- [ ] Refactor / code cleanup
	- [ ] Build or config change
	- [ ] Other (please describe below)

	---

	## Checklist
	Before submitting, please confirm that you have:

	- [ ] Read and followed the [CONTRIBUTING.md](../CONTRIBUTING.md) guidelines
	- [ ] Tested the changes locally
	- [ ] Updated relevant documentation (if applicable)
	- [ ] Linked any related issues

	---

	### Additional Notes
	Add any extra context or information about the PR here (optional).

	---

	Thank you for contributing to Laddr! 🧠