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

Libraries

How to use Chickaboo/ChickaQ with Transformers:

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

pipe = pipeline("text-generation", model="Chickaboo/ChickaQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Chickaboo/ChickaQ")
model = AutoModelForCausalLM.from_pretrained("Chickaboo/ChickaQ")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Chickaboo/ChickaQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Chickaboo/ChickaQ

SGLang

How to use Chickaboo/ChickaQ 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 "Chickaboo/ChickaQ" \
    --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": "Chickaboo/ChickaQ",
		"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 "Chickaboo/ChickaQ" \
        --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": "Chickaboo/ChickaQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

ChickaQ

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---
base_model:
- vilm/Quyen-SE-v0.1
- Qwen/Qwen1.5-0.5B-Chat
library_name: transformers
tags:
- mergekit
- merge
license: mit
---
# Models in the ChickaQ family
- **ChickaQ (0.5B)**
- **ChickaQ-Large (1.8B)**

- **ChickaQ-V2-Beta (0.9B)**
- **ChickaQ-V2-Large-Beta (3B)**
# mergedmodel

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [vilm/Quyen-SE-v0.1](https://huggingface.co/vilm/Quyen-SE-v0.1) as a base.

### Models Merged

The following models were included in the merge:
* [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: vilm/Quyen-SE-v0.1
    # no parameters necessary for base model
  - model: Qwen/Qwen1.5-0.5B-Chat
    parameters:
      density: 0.5
      weight: 0.5
merge_method: ties
base_model: vilm/Quyen-SE-v0.1
parameters:
  normalize: true
dtype: float16

```