Instructions to use artificialguybr/GenStructDolphin-7B-Slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use artificialguybr/GenStructDolphin-7B-Slerp with Transformers:

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

pipe = pipeline("text-generation", model="artificialguybr/GenStructDolphin-7B-Slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("artificialguybr/GenStructDolphin-7B-Slerp")
model = AutoModelForCausalLM.from_pretrained("artificialguybr/GenStructDolphin-7B-Slerp", device_map="auto")
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 artificialguybr/GenStructDolphin-7B-Slerp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "artificialguybr/GenStructDolphin-7B-Slerp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "artificialguybr/GenStructDolphin-7B-Slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/artificialguybr/GenStructDolphin-7B-Slerp

SGLang

How to use artificialguybr/GenStructDolphin-7B-Slerp 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 "artificialguybr/GenStructDolphin-7B-Slerp" \
    --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": "artificialguybr/GenStructDolphin-7B-Slerp",
		"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 "artificialguybr/GenStructDolphin-7B-Slerp" \
        --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": "artificialguybr/GenStructDolphin-7B-Slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use artificialguybr/GenStructDolphin-7B-Slerp with Docker Model Runner:
```
docker model run hf.co/artificialguybr/GenStructDolphin-7B-Slerp
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GenStructDolphin-7B-Slerp

GenStructDolphin-7B-Slerp is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
      - model: NousResearch/Genstruct-7B
        layer_range: [0, 32]
      - model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser
        layer_range: [0, 32]
merge_method: slerp
base_model: NousResearch/Genstruct-7B

---
### 🌐 Website
You can find more of my models, projects, and information on my official website:
- **[artificialguy.com](https://artificialguy.com/)**


### 🚀 Prompt Hub
Need high-quality prompts for image models and LLMs? Explore **[findgoodprompt.com](https://findgoodprompt.com)**.
### 💖 Support My Work
If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.
- **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187)
- **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
- **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape)
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "artificialguybr/GenStructDolphin-7B-Slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Explore More

Discover more of my models, projects, and practical AI resources:

AI tools directory: JustGoodAI — a curated directory of useful AI tools.

Downloads last month: 13

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for artificialguybr/GenStructDolphin-7B-Slerp

NousResearch/Genstruct-7B

dphn/dolphin-2.6-mistral-7b-dpo-laser

Merge model

this model

Quantizations

1 model