Instructions to use qgallouedec/my_dir2_2_merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qgallouedec/my_dir2_2_merged with Transformers:

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

pipe = pipeline("text-generation", model="qgallouedec/my_dir2_2_merged")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("qgallouedec/my_dir2_2_merged")
model = AutoModelForMultimodalLM.from_pretrained("qgallouedec/my_dir2_2_merged")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use qgallouedec/my_dir2_2_merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qgallouedec/my_dir2_2_merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qgallouedec/my_dir2_2_merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/qgallouedec/my_dir2_2_merged

SGLang

How to use qgallouedec/my_dir2_2_merged 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 "qgallouedec/my_dir2_2_merged" \
    --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": "qgallouedec/my_dir2_2_merged",
		"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 "qgallouedec/my_dir2_2_merged" \
        --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": "qgallouedec/my_dir2_2_merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use qgallouedec/my_dir2_2_merged with Docker Model Runner:
```
docker model run hf.co/qgallouedec/my_dir2_2_merged
```

merged

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

my_dir2/checkpoint-2
trl-internal-testing/tiny-random-LlamaForCausalLM

Configuration

The following YAML configuration was used to produce this model:

dtype: float16
merge_method: linear
slices:
- sources:
  - layer_range: [0, 2]
    model: my_dir2/checkpoint-2
    parameters:
      weight: 0.5
  - layer_range: [0, 2]
    model: trl-internal-testing/tiny-random-LlamaForCausalLM
    parameters:
      weight: 0.5

Downloads last month: 4

Safetensors

Model size

1.03M params

Tensor type

F16

Model tree for qgallouedec/my_dir2_2_merged

Base model

trl-internal-testing/tiny-random-LlamaForCausalLM

Finetuned

(14)

this model

Paper for qgallouedec/my_dir2_2_merged

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 8