Instructions to use Pedro13543/experimental_merge_no4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pedro13543/experimental_merge_no4 with Transformers:

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

pipe = pipeline("text-generation", model="Pedro13543/experimental_merge_no4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Pedro13543/experimental_merge_no4")
model = AutoModelForMultimodalLM.from_pretrained("Pedro13543/experimental_merge_no4")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Pedro13543/experimental_merge_no4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pedro13543/experimental_merge_no4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pedro13543/experimental_merge_no4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pedro13543/experimental_merge_no4

SGLang

How to use Pedro13543/experimental_merge_no4 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 "Pedro13543/experimental_merge_no4" \
    --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": "Pedro13543/experimental_merge_no4",
		"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 "Pedro13543/experimental_merge_no4" \
        --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": "Pedro13543/experimental_merge_no4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Pedro13543/experimental_merge_no4 with Docker Model Runner:
```
docker model run hf.co/Pedro13543/experimental_merge_no4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

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

Merge Details

Merge Method

This model was merged using the SCE merge method using Pedro13543/good_mix_r1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  # Pivot model
  - model: bluuwhale/L3-SthenoMaidBlackroot-8B-V1
  # Target models
  - model: huihui-ai/DeepSeek-R1-Distill-Llama-8B-abliterated
  - model: Skywork/Skywork-o1-Open-Llama-3.1-8B
  - model: Solshine/reflection-llama-3.1-8B-Solshine-Full
  - model : Undi95/Meta-Llama-3.1-8B-Claude
  - model : TheDrummer/Llama-3SOME-8B-v2

merge_method: sce
base_model: Pedro13543/good_mix_r1
parameters:
  select_topk: 1.0
dtype: bfloat16