Instructions to use jeiku/Bones_3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeiku/Bones_3B with Transformers:

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

pipe = pipeline("text-generation", model="jeiku/Bones_3B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jeiku/Bones_3B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jeiku/Bones_3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeiku/Bones_3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeiku/Bones_3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jeiku/Bones_3B

SGLang

How to use jeiku/Bones_3B 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 "jeiku/Bones_3B" \
    --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": "jeiku/Bones_3B",
		"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 "jeiku/Bones_3B" \
        --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": "jeiku/Bones_3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jeiku/Bones_3B with Docker Model Runner:
```
docker model run hf.co/jeiku/Bones_3B
```

Bones_3B

File size: 1,144 Bytes

89f7414

---
base_model:
- cxllin/StableHermes-3b
- jondurbin/airoboros-3b-3p0
- jeiku/Rosa_v1_3B
tags:
- mergekit
- merge

---
# great

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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [jeiku/Rosa_v1_3B](https://huggingface.co/jeiku/Rosa_v1_3B) as a base.

### Models Merged

The following models were included in the merge:
* [cxllin/StableHermes-3b](https://huggingface.co/cxllin/StableHermes-3b)
* [jondurbin/airoboros-3b-3p0](https://huggingface.co/jondurbin/airoboros-3b-3p0)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: cxllin/StableHermes-3b
    parameters:
      weight: 0.33
      density: 1
  - model: jondurbin/airoboros-3b-3p0
    parameters:
      weight: 0.33
      density: 1
  - model: jeiku/Rosa_v1_3B
    parameters:
      weight: 0.33
      density: 1
merge_method: dare_ties
base_model: jeiku/Rosa_v1_3B
parameters:
dtype: bfloat16
```