Instructions to use vericava/llm-jp-3-vericava-posts-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vericava/llm-jp-3-vericava-posts-v1 with Transformers:

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

pipe = pipeline("text-generation", model="vericava/llm-jp-3-vericava-posts-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vericava/llm-jp-3-vericava-posts-v1")
model = AutoModelForCausalLM.from_pretrained("vericava/llm-jp-3-vericava-posts-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vericava/llm-jp-3-vericava-posts-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vericava/llm-jp-3-vericava-posts-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vericava/llm-jp-3-vericava-posts-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vericava/llm-jp-3-vericava-posts-v1

SGLang

How to use vericava/llm-jp-3-vericava-posts-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 "vericava/llm-jp-3-vericava-posts-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vericava/llm-jp-3-vericava-posts-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "vericava/llm-jp-3-vericava-posts-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vericava/llm-jp-3-vericava-posts-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vericava/llm-jp-3-vericava-posts-v1 with Docker Model Runner:
```
docker model run hf.co/vericava/llm-jp-3-vericava-posts-v1
```

llm-jp-3-vericava-posts-v1

This model is a fine-tuned version of llm-jp/llm-jp-3-3.7b on the dataset of my posts on the Internet.

Model description

It generates text resembling what I post on the Internet.

Intended uses & limitations

CAUTION: It may produce something I'd never say. I do not impose any restriction(s) on the use of this model.

Training and evaluation data

Twitter/X: https://x.com/vericava

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 300
num_epochs: 4

Training results

Framework versions

Transformers 4.55.0
Pytorch 2.8.0+cu128
Datasets 4.0.0
Tokenizers 0.21.4

Downloads last month: 9

Safetensors

Model size

4B params

Tensor type

F32

Model tree for vericava/llm-jp-3-vericava-posts-v1

Base model

llm-jp/llm-jp-3-3.7b

Finetuned

(13)

this model

vericava
/

llm-jp-3-vericava-posts-v1