Instructions to use P0u4a/maincoder-1b-toolcalling-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use P0u4a/maincoder-1b-toolcalling-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Maincode/Maincoder-1B")
model = PeftModel.from_pretrained(base_model, "P0u4a/maincoder-1b-toolcalling-lora")

Transformers

How to use P0u4a/maincoder-1b-toolcalling-lora with Transformers:

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

pipe = pipeline("text-generation", model="P0u4a/maincoder-1b-toolcalling-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("P0u4a/maincoder-1b-toolcalling-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use P0u4a/maincoder-1b-toolcalling-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "P0u4a/maincoder-1b-toolcalling-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "P0u4a/maincoder-1b-toolcalling-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/P0u4a/maincoder-1b-toolcalling-lora

SGLang

How to use P0u4a/maincoder-1b-toolcalling-lora 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 "P0u4a/maincoder-1b-toolcalling-lora" \
    --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": "P0u4a/maincoder-1b-toolcalling-lora",
		"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 "P0u4a/maincoder-1b-toolcalling-lora" \
        --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": "P0u4a/maincoder-1b-toolcalling-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use P0u4a/maincoder-1b-toolcalling-lora with Docker Model Runner:
```
docker model run hf.co/P0u4a/maincoder-1b-toolcalling-lora
```

maincoder-1b-toolcalling-lora

File size: 1,546 Bytes

2ad4b4e

---
base_model: Maincode/Maincoder-1B
library_name: peft
model_name: maincoder-1b-toolcalling-lora
tags:
- base_model:adapter:Maincode/Maincoder-1B
- lora
- sft
- transformers
- trl
licence: license
pipeline_tag: text-generation
---

# Model Card for maincoder-1b-toolcalling-lora

This model is a fine-tuned version of [Maincode/Maincoder-1B](https://huggingface.co/Maincode/Maincoder-1B).
It has been trained using [TRL](https://github.com/huggingface/trl).

## Quick start

```python
from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```

## Training procedure

 


This model was trained with SFT.

### Framework versions

- PEFT 0.17.1
- TRL: 0.21.0
- Transformers: 4.57.3
- Pytorch: 2.10.0+cu128
- Datasets: 4.8.4
- Tokenizers: 0.22.2

## Citations



Cite TRL as:
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```