Instructions to use rovdetection/code-1b-chat-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rovdetection/code-1b-chat-v2 with Transformers:

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

pipe = pipeline("text-generation", model="rovdetection/code-1b-chat-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rovdetection/code-1b-chat-v2")
model = AutoModelForCausalLM.from_pretrained("rovdetection/code-1b-chat-v2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rovdetection/code-1b-chat-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rovdetection/code-1b-chat-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rovdetection/code-1b-chat-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rovdetection/code-1b-chat-v2

SGLang

How to use rovdetection/code-1b-chat-v2 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 "rovdetection/code-1b-chat-v2" \
    --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": "rovdetection/code-1b-chat-v2",
		"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 "rovdetection/code-1b-chat-v2" \
        --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": "rovdetection/code-1b-chat-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rovdetection/code-1b-chat-v2 with Docker Model Runner:
```
docker model run hf.co/rovdetection/code-1b-chat-v2
```

code-1b-chat-v2 / README.md

rovdetection

Model save

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	---
	library_name: transformers
	license: apache-2.0
	base_model: rovdetection/code-1b-pretrain-v3
	tags:
	- generated_from_trainer
	model-index:
	- name: code-1b-chat-v2
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# code-1b-chat-v2

	This model is a fine-tuned version of [rovdetection/code-1b-pretrain-v3](https://huggingface.co/rovdetection/code-1b-pretrain-v3) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9919

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 2e-05
	- train_batch_size: 1
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 32
	- total_train_batch_size: 32
	- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 100
	- training_steps: 1000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|
	\| 1.0509 \| 0.8840 \| 500 \| 1.0258 \|
	\| 0.6652 \| 1.7673 \| 1000 \| 0.9919 \|


	### Framework versions

	- Transformers 5.9.0
	- Pytorch 2.10.0+cu128
	- Datasets 4.8.5
	- Tokenizers 0.22.2