Instructions to use thaddickson/Delphi-7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thaddickson/Delphi-7B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="thaddickson/Delphi-7B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thaddickson/Delphi-7B-v1")
model = AutoModelForCausalLM.from_pretrained("thaddickson/Delphi-7B-v1")
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 thaddickson/Delphi-7B-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/thaddickson/Delphi-7B-v1

SGLang

How to use thaddickson/Delphi-7B-v1 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 "thaddickson/Delphi-7B-v1" \
    --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": "thaddickson/Delphi-7B-v1",
		"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 "thaddickson/Delphi-7B-v1" \
        --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": "thaddickson/Delphi-7B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thaddickson/Delphi-7B-v1 with Docker Model Runner:
```
docker model run hf.co/thaddickson/Delphi-7B-v1
```

thaddickson commited on Mar 16

Commit

9444058

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@@ -26,7 +26,7 @@ base_model:
 ## The Model
-Delphi-7B is a 7.6B parameter reasoning model built on a 6-model merge of Qwen 2.5 7B specialists, trained with mixed-domain SFT on 5090s and H100 GPUs. It combines math reasoning, chain-of-thought logic, and instruction following into a single general-purpose model — then seasons it with hand-written expert reasoning pairs from two decades of healthcare cybersecurity work.
 This is Chapter 1 of a three-part build. The general model proves the pipeline. What comes next is the point.

 ## The Model
+Delphi-7B is a 7.6B parameter reasoning model built on a 6-model merge of Qwen 2.5 7B specialists, trained with mixed-domain SFT on 5090s and H100 GPUs in a weekend. It combines math reasoning, chain-of-thought logic, and instruction following into a single general-purpose model — then seasons it with hand-written expert reasoning pairs from two decades of healthcare cybersecurity work.
 This is Chapter 1 of a three-part build. The general model proves the pipeline. What comes next is the point.