Instructions to use aimeri/name-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aimeri/name-generator with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aimeri/name-generator")
model = AutoModelForCausalLM.from_pretrained("aimeri/name-generator", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aimeri/name-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/aimeri/name-generator

SGLang

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

Unsloth Desktop
Docker Model Runner
How to use aimeri/name-generator with Docker Model Runner:
```
docker model run hf.co/aimeri/name-generator
```

aimeri commited on 9 days ago

Commit

32014ca

verified ·

1 Parent(s): b0a06ca

Point to v2; link the v1 dataset revision

Browse files

Files changed (1) hide show

README.md +4 -1

README.md CHANGED Viewed

@@ -24,6 +24,9 @@ requests like "a Brazilian man's name" or "a woman from the Ottoman Empire" with
 naming data, instead of the handful of names language models keep reaching for (Elara Voss, Kael
 Thorne, Dr. Sarah Chen).
 GGUF quants (Q8_0, Q6_K, Q5_K_M, Q4_K_M) for llama.cpp, LM Studio and similar:
 [aimeri/name-generator-GGUF](https://huggingface.co/aimeri/name-generator-GGUF)
@@ -91,7 +94,7 @@ Through an OpenAI-compatible server (llama.cpp, vLLM, LM Studio) send a single u
 | | |
 |---|---|
 | Base | [aimeri/Ministral-3-3B-Base-2512-chatml](https://huggingface.co/aimeri/Ministral-3-3B-Base-2512-chatml): the text-only Ministral-3-3B-Base-2512 with `<|im_start|>`/`<|im_end|>` claimed for ChatML |
-| Data | [aimeri/name-generator](https://huggingface.co/datasets/aimeri/name-generator): 8,536 training and 459 validation examples covering 333 countries and historical states (24% historical) |
 | Method | Supervised fine-tuning with Unsloth and TRL, full fine-tune, loss on the assistant turn only |
 | Hyperparameters | learning rate 1e-5, linear schedule, 5% warmup, 1 epoch (2,134 steps), effective batch size 4, AdamW 8-bit, weight decay 0.001 |
 | Software | transformers 5.20.2, Unsloth 2026.10.9 |

 naming data, instead of the handful of names language models keep reaching for (Elara Voss, Kael
 Thorne, Dr. Sarah Chen).
+> **v2 is out:** [aimeri/name-generator-v2](https://huggingface.co/aimeri/name-generator-v2) also
+> invents fantasy names (elves, dwarves, orcs, dragons, fantasy kingdoms) and keeps the real ones.
 GGUF quants (Q8_0, Q6_K, Q5_K_M, Q4_K_M) for llama.cpp, LM Studio and similar:
 [aimeri/name-generator-GGUF](https://huggingface.co/aimeri/name-generator-GGUF)
 | | |
 |---|---|
 | Base | [aimeri/Ministral-3-3B-Base-2512-chatml](https://huggingface.co/aimeri/Ministral-3-3B-Base-2512-chatml): the text-only Ministral-3-3B-Base-2512 with `<|im_start|>`/`<|im_end|>` claimed for ChatML |
+| Data | [aimeri/name-generator](https://huggingface.co/datasets/aimeri/name-generator/tree/v1), tag `v1`: 8,536 training and 459 validation examples covering 333 countries and historical states (24% historical) |
 | Method | Supervised fine-tuning with Unsloth and TRL, full fine-tune, loss on the assistant turn only |
 | Hyperparameters | learning rate 1e-5, linear schedule, 5% warmup, 1 epoch (2,134 steps), effective batch size 4, AdamW 8-bit, weight decay 0.001 |
 | Software | transformers 5.20.2, Unsloth 2026.10.9 |