python -m vllm.entrypoints.openai.api_server \
    --model neutrino12/tensorstax-sft-unsloth-32b-dora32-3020 \
    --host 0.0.0.0 \
    --port 8000 \
    --max-model-len 32768 \
    --trust-remote-code

Usage with Transformers

Base Model: Qwen/Qwen2.5-Coder-32B-Instruct
Fine-tuning Method: LoRA (merged into full weights)
Task: Code generation for DBT SQL models
Training Framework: Unsloth + Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "neutrino12/tensorstax-sft-unsloth-32b-dora32-3020",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "neutrino12/tensorstax-sft-unsloth-32b-dora32-3020",
    trust_remote_code=True
)

# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Training Details

This model was fine-tuned using LoRA with the following configuration:

LoRA Rank: 32
LoRA Alpha: 64
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Learning Rate: 1e-5
Sequence Length: 8000

The LoRA adapter was then merged back into the full model weights for easier deployment.