Instructions to use chihoonlee10/T3Q-Merge-SOLAR12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chihoonlee10/T3Q-Merge-SOLAR12 with Transformers:

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

pipe = pipeline("text-generation", model="chihoonlee10/T3Q-Merge-SOLAR12")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("chihoonlee10/T3Q-Merge-SOLAR12")
model = AutoModelForMultimodalLM.from_pretrained("chihoonlee10/T3Q-Merge-SOLAR12")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chihoonlee10/T3Q-Merge-SOLAR12 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chihoonlee10/T3Q-Merge-SOLAR12"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chihoonlee10/T3Q-Merge-SOLAR12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chihoonlee10/T3Q-Merge-SOLAR12

SGLang

How to use chihoonlee10/T3Q-Merge-SOLAR12 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 "chihoonlee10/T3Q-Merge-SOLAR12" \
    --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": "chihoonlee10/T3Q-Merge-SOLAR12",
		"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 "chihoonlee10/T3Q-Merge-SOLAR12" \
        --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": "chihoonlee10/T3Q-Merge-SOLAR12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chihoonlee10/T3Q-Merge-SOLAR12 with Docker Model Runner:
```
docker model run hf.co/chihoonlee10/T3Q-Merge-SOLAR12
```

T3Q-Merge-SOLAR12

File size: 878 Bytes

f58075f
 
 
ac02755
de4ddf8
ac02755
de4ddf8
558ea71
de4ddf8
 
 
 
 
 
 
 
 
 
 
558ea71
de4ddf8

---
license: apache-2.0
---
## T3Q-Merge-SOLAR12

T3Q-Merge-SOLAR12 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [davidkim205/komt-solar-10.7b-sft-v5](https://huggingface.co/davidkim205/komt-solar-10.7b-sft-v5)
* [hyeogi/SOLAR-10.7B-v1.5](https://huggingface.co/hyeogi/SOLAR-10.7B-v1.5)
  

**Model Developers** Chihoon Lee(chlee10), T3Q

  
```yaml
  
  slices:
      - sources:
        - model: davidkim205/komt-solar-10.7b-sft-v5
          layer_range: [0, 48]
        - model: hyeogi/SOLAR-10.7B-v1.5
          layer_range: [0, 48]
  
  merge_method: slerp
  base_model: davidkim205/komt-solar-10.7b-sft-v5
  
  parameters:
   t:
      - filter: self_attn
        value: [0, 0.5, 0.3, 0.7, 1]
      - filter: mlp
        value: [1, 0.5, 0.7, 0.3, 0]
      - value: 0.5 # fallback for rest of tensors
      
  dtype: bfloat16

```