Instructions to use vivek1192/merged_llamamedicalQAdella-hindi_rev1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vivek1192/merged_llamamedicalQAdella-hindi_rev1 with Transformers:

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

pipe = pipeline("text-generation", model="vivek1192/merged_llamamedicalQAdella-hindi_rev1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("vivek1192/merged_llamamedicalQAdella-hindi_rev1")
model = AutoModelForMultimodalLM.from_pretrained("vivek1192/merged_llamamedicalQAdella-hindi_rev1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vivek1192/merged_llamamedicalQAdella-hindi_rev1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vivek1192/merged_llamamedicalQAdella-hindi_rev1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vivek1192/merged_llamamedicalQAdella-hindi_rev1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vivek1192/merged_llamamedicalQAdella-hindi_rev1

SGLang

How to use vivek1192/merged_llamamedicalQAdella-hindi_rev1 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 "vivek1192/merged_llamamedicalQAdella-hindi_rev1" \
    --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": "vivek1192/merged_llamamedicalQAdella-hindi_rev1",
		"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 "vivek1192/merged_llamamedicalQAdella-hindi_rev1" \
        --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": "vivek1192/merged_llamamedicalQAdella-hindi_rev1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vivek1192/merged_llamamedicalQAdella-hindi_rev1 with Docker Model Runner:
```
docker model run hf.co/vivek1192/merged_llamamedicalQAdella-hindi_rev1
```

merged_models

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

Merge Details

Merge Method

This model was merged using the DELLA merge method using meta-llama/Meta-Llama-3-8B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: meta-llama/Meta-Llama-3-8B
dtype: float16
merge_method: della
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 32]
        model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
        parameters:
          density: 0.5
          weight: 0.5
      - layer_range: [0, 32]
        model: Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1
        parameters:
          density: 0.5
          weight: 0.5
      - layer_range: [0, 32]
        model: meta-llama/Meta-Llama-3-8B
parameters:
  int8_mask: 1.0
  normalize: 0.0

Downloads last month: 1

Safetensors

Model size

8B params

Tensor type

F16

Model tree for vivek1192/merged_llamamedicalQAdella-hindi_rev1

Cognitive-Lab/LLama3-Gaja-Hindi-8B-v0.1

johnsnowlabs/JSL-MedLlama-3-8B-v2.0

meta-llama/Meta-Llama-3-8B

Merge model

this model

Paper for vivek1192/merged_llamamedicalQAdella-hindi_rev1

DELLA-Merging: Reducing Interference in Model Merging through Magnitude-Based Sampling

Paper • 2406.11617 • Published Jun 17, 2024 • 10