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| Feature | Details |
|---|---|
| Base Model | Qwen2.5-3B-Instruct |
| Parameters | 3B |
| Fine-Tuning Method | QLoRA (4-bit, r=16, alpha=32) |
| Context Length | 2048 tokens |
| License | Apache 2.0 |
| Author | Vedant Deore |
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "vedantdeore/VedantDev-V0.1-3B-Instruct"
4
5tokenizer = AutoTokenizer.from_pretrained(model_name)
6model = AutoModelForCausalLM.from_pretrained(
7 model_name,
8 torch_dtype="auto",
9 device_map="auto",
10)
11
12messages = [
13 {"role": "system", "content": "You are VedantDev, an AI assistant created by Vedant Deore."},
14 {"role": "user", "content": "Who are you?"},
15]
16
17text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
18inputs = tokenizer([text], return_tensors="pt").to(model.device)
19outputs = model.generate(**inputs, max_new_tokens=256)
20response = tokenizer.batch_decode(outputs[:, inputs.input_ids.shape[-1]:], skip_special_tokens=True)[0]
21print(response)| Model | Status | Description |
|---|---|---|
| VedantDev-V0.1-3B-Instruct | Released | Test release, Qwen2.5-3B base |
| VedantDev-V1-7B-Instruct | Planned | Production release, 7B base |
| VedantDev-V1-70B-Instruct | Planned | Full production, 70B base |
| Kairos-V1-70B-Instruct | Planned | NGO domain-specific model |
1ollama create vedantdev -f Modelfile
2ollama run vedantdev1@misc{vedantdev2026,
2 title = {VedantDev-V0.1-3B-Instruct: A Fine-Tuned Language Model},
3 author = {Vedant Deore},
4 year = {2026},
5 url = {https://huggingface.co/vedantdeore/VedantDev-V0.1-3B-Instruct}
6}