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Model: nn-tech/MetalGPT-1-AWQ
Source: Original Platform

README.md

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+---
+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)
+
+```
+