Instructions to use Hjgugugjhuhjggg/mergekit-ties-xzdpqzs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hjgugugjhuhjggg/mergekit-ties-xzdpqzs with Transformers:

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

pipe = pipeline("text-generation", model="Hjgugugjhuhjggg/mergekit-ties-xzdpqzs")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hjgugugjhuhjggg/mergekit-ties-xzdpqzs")
model = AutoModelForCausalLM.from_pretrained("Hjgugugjhuhjggg/mergekit-ties-xzdpqzs")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hjgugugjhuhjggg/mergekit-ties-xzdpqzs with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hjgugugjhuhjggg/mergekit-ties-xzdpqzs"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hjgugugjhuhjggg/mergekit-ties-xzdpqzs",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hjgugugjhuhjggg/mergekit-ties-xzdpqzs

SGLang

How to use Hjgugugjhuhjggg/mergekit-ties-xzdpqzs 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 "Hjgugugjhuhjggg/mergekit-ties-xzdpqzs" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hjgugugjhuhjggg/mergekit-ties-xzdpqzs",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Hjgugugjhuhjggg/mergekit-ties-xzdpqzs" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hjgugugjhuhjggg/mergekit-ties-xzdpqzs",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Hjgugugjhuhjggg/mergekit-ties-xzdpqzs with Docker Model Runner:
```
docker model run hf.co/Hjgugugjhuhjggg/mergekit-ties-xzdpqzs
```

merge

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

Merge Details

Merge Method

This model was merged using the TIES merge method using meta-llama/Llama-3.2-1B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: ties
architectures: ["transformer"]
base_model: meta-llama/Llama-3.2-1B-Instruct

models:
  - model: Alelcv27/llama3.2-1b-math-code
  - model: huyhoangt2201/llama-3.2-1b-sql_finetuned_billingual_3.0_merged
  - model: autoprogrammer/Llama-3.2-1B-Instruct-MGSM8K-sft1
  - model: meta-llama/Llama-3.2-1B-Instruct
  - model: autoprogrammer/Llama-3.2-1B-Instruct-medmcqa-zh-linear
  - model: meta-llama/Llama-3.2-1B
  - model: unsloth/Llama-3.2-1B-Instruct-bnb-4bit
  - model: MLking2/llama-3.2-1b-medical
  - model: jayavibhav/llama3.2_1b_CoT
  - model: huyhoangt2201/llama-3.2-1b-chat-sql3-merged
  - model: student-abdullah/Llama3.2-1B_Hinglish-Medicine-Dataset_Finetuning_28-09

parameters:
  density: 0.5
  weight: 1.0
  int8_mask: true
  normalize: true