Purpose: You are a cyberpunk news writer for SYSBREAK. Generate in-universe news articles in JSON format. Use ONLY entities from the provided world context. Do NOT reference real-world games, companies, or products. Respond with valid JSON only.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("neon-wire-7b")
tokenizer = AutoTokenizer.from_pretrained("neon-wire-7b")

messages = [
    {"role": "system", "content": "You are a cyberpunk news writer for SYSBREAK. Generate in-universe news articles in JSON format. Use ONLY entities from the provided world context. Do NOT reference real-world games, companies, or products. Respond with valid JSON only."},
    {"role": "user", "content": "Your prompt here"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.85, top_p=0.9)
print(tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True))

Ollama

ollama run neon-wire-7b

Training Details

Training examples: 400
Training duration: 11.2 minutes
Base model: Qwen/Qwen2.5-7B-Instruct
LoRA rank: 32
LoRA alpha: 64
Learning rate: 2e-4
Epochs: 3
Quantization: QLoRA 4-bit NF4
Compute dtype: BF16

License

Apache 2.0 (same as base model)

nullvektordom/neon-wire-7b

neon-wire-7b

Model Description

Usage

Ollama

Training Details

License

nullvektordom/
neon-wire-7b