This model was fine-tuned with PEFT/LoRA and merged into a standalone set of weights for deployment. It focuses on short, structured dialogue, refusal behavior for unsafe requests, and instruction adherence in tool-style prompts. Fine-tuned from gemma-3-27b-it. The model config includes vision components and image token settings, indicating image+text capability.
1import torch
2from transformers import AutoTokenizer, AutoModelForCausalLM
3
4model_id = "dodotechnologies/dodo-v1-beta"
5
6tokenizer = AutoTokenizer.from_pretrained(model_id)
7model = AutoModelForCausalLM.from_pretrained(
8 model_id,
9 torch_dtype=torch.bfloat16,
10 device_map="auto",
11)
12model.eval()
13
14prompt = "User: Explain LoRA in three bullets.\nAssistant:"
15inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
16with torch.no_grad():
17 output = model.generate(**inputs, max_new_tokens=128, do_sample=False)
18
19print(tokenizer.decode(output[0], skip_special_tokens=True))
1curl -X POST \
2 -H "Authorization: Bearer $HF_TOKEN" \
3 -H "Content-Type: application/json" \
4 https://api-inference.huggingface.co/models/dodotechnologies/dodo-v1-beta \
5 -d '{
6 "inputs": "User: Explain LoRA in three bullets.\nAssistant:",
7 "parameters": {"max_new_tokens": 128, "do_sample": false}
8 }'
Run a private API service on a GPU host and expose it only to authorized clients.
1curl -X POST http://<host>:<port>/v1/chat \
2 -H "Content-Type: application/json" \
3 -d '{
4 "prompt": "User: Explain LLM finetuning.\nAssistant:",
5 "max_new_tokens": 128,
6 "do_sample": false
7 }'