Instructions to use nikhilkumar42/model_sft_resta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nikhilkumar42/model_sft_resta with Transformers:

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

pipe = pipeline("text-generation", model="nikhilkumar42/model_sft_resta")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nikhilkumar42/model_sft_resta")
model = AutoModelForCausalLM.from_pretrained("nikhilkumar42/model_sft_resta")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nikhilkumar42/model_sft_resta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nikhilkumar42/model_sft_resta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nikhilkumar42/model_sft_resta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nikhilkumar42/model_sft_resta

SGLang

How to use nikhilkumar42/model_sft_resta 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 "nikhilkumar42/model_sft_resta" \
    --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": "nikhilkumar42/model_sft_resta",
		"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 "nikhilkumar42/model_sft_resta" \
        --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": "nikhilkumar42/model_sft_resta",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nikhilkumar42/model_sft_resta with Docker Model Runner:
```
docker model run hf.co/nikhilkumar42/model_sft_resta
```

model_sft_resta

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

Merge Details

Merge Method

This model was merged using the Task Arithmetic merge method using nikhilkumar42/model_harmful_full as a base.

Models Merged

The following models were included in the merge:

Qwen/Qwen2.5-1.5B-Instruct
./full_weights_model_sft_lora

Configuration

The following YAML configuration was used to produce this model:

merge_method: task_arithmetic
base_model: nikhilkumar42/model_harmful_full
parameters:
    weight: 1.0
models:
  - model: ./full_weights_model_sft_lora
    parameters:
        weight: 1.0
  - model: Qwen/Qwen2.5-1.5B-Instruct
    parameters:
        weight: 1.0
dtype: bfloat16

Downloads last month: 9

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for nikhilkumar42/model_sft_resta

Qwen/Qwen2.5-1.5B-Instruct

nikhilkumar42/model_harmful_full

Merge model

this model

Paper for nikhilkumar42/model_sft_resta

Editing Models with Task Arithmetic

Paper • 2212.04089 • Published Dec 8, 2022 • 8