Instructions to use testmoto/gemma-2-9b-platypus-coding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use testmoto/gemma-2-9b-platypus-coding with Transformers:

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

pipe = pipeline("text-generation", model="testmoto/gemma-2-9b-platypus-coding")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("testmoto/gemma-2-9b-platypus-coding")
model = AutoModelForCausalLM.from_pretrained("testmoto/gemma-2-9b-platypus-coding", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use testmoto/gemma-2-9b-platypus-coding with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "testmoto/gemma-2-9b-platypus-coding"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "testmoto/gemma-2-9b-platypus-coding",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/testmoto/gemma-2-9b-platypus-coding

SGLang

How to use testmoto/gemma-2-9b-platypus-coding 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 "testmoto/gemma-2-9b-platypus-coding" \
    --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": "testmoto/gemma-2-9b-platypus-coding",
		"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 "testmoto/gemma-2-9b-platypus-coding" \
        --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": "testmoto/gemma-2-9b-platypus-coding",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use testmoto/gemma-2-9b-platypus-coding with Docker Model Runner:
```
docker model run hf.co/testmoto/gemma-2-9b-platypus-coding
```

gemma-2-9b-platypus-coding

File size: 1,268 Bytes

aa9dca4

---
base_model:
- google/gemma-2-9b
- testmoto/gemma-2-9b-synthetic_coding
- testmoto/gemma-2-9b-platypus-02
library_name: transformers
tags:
- mergekit
- merge

---
# merge

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 [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b) as a base.

### Models Merged

The following models were included in the merge:
* [testmoto/gemma-2-9b-synthetic_coding](https://huggingface.co/testmoto/gemma-2-9b-synthetic_coding)
* [testmoto/gemma-2-9b-platypus-02](https://huggingface.co/testmoto/gemma-2-9b-platypus-02)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: google/gemma-2-9b
    # No parameters necessary for base model
  - model: testmoto/gemma-2-9b-platypus-02
    parameters:
      density: 0.53
      weight: 0.5
  - model: testmoto/gemma-2-9b-synthetic_coding
    parameters:
      density: 0.53
      weight: 0.5
merge_method: dare_ties
base_model: google/gemma-2-9b
parameters:
  int8_mask: true
dtype: bfloat16

```