README.md

---
pipeline_tag: text-generation
library_name: transformers
tags:
- mining
- awq
license: cc-by-nc-sa-4.0
language:
- ru
base_model: nn-tech/MetalGPT-1
---

## Description

**MetalGPT-1** is a model built upon the Qwen/Qwen3-32B and incorporates both continual pre-training and supervised fine-tuning on domain-specific data from the mining and metallurgy industry.

---

### Quantization

For convenience and better efficiency, we also offer this AWQ-quantized checkpoint of the nn-tech/MetalGPT-1 model. Using AWQ 4-bit quantization greatly speeds up inference and reduces memory consumption, without significant impact on quality.

---

### HF Usage

```python

from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer
import torch

torch.manual_seed(42)

model_name = "nn-tech/MetalGPT-1-AWQ"

tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
model = AutoAWQForCausalLM.from_quantized(
    model_name,
    device_map="auto",
)

messages=[
    {"role": "system", "content": "Ты специалист в области металлургии."},
    {"role": "user", "content": "Назови плюсы и минусы хлоридной и сульфатной технологии производства никеля."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    # enable_thinking=False
)

device = next(model.parameters()).device
model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.7,
)

# Обрезаем префикс промпта
generated_ids = [
    output_ids[len(input_ids):]
    for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(
    generated_ids,
    skip_special_tokens=True
)[0]

print(response)

```

---

### VLLM usage

```bash
vllm serve nn-tech/MetalGPT-1-AWQ --reasoning-parser qwen3

```

```python

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="dummy"  
)

response = client.chat.completions.create(
    model="nn-tech/MetalGPT-1-AWQ",
    messages=[
        {"role": "system", "content": "Ты специалист в области металлургии."},
        {"role": "user", "content": "Назови плюсы и минусы хлоридной и сульфатной технологии производства никеля."}
    ],
    temperature=0.7,
    max_tokens=1024
)

print(response.choices[0].message.content)

```