Instructions to use Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-8B-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005")

Transformers

How to use Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005

SGLang

How to use Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005",
    max_seq_length=2048,
)

Docker Model Runner
How to use Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005 with Docker Model Runner:
```
docker model run hf.co/Fermi37/railway-qwen3-8b-lora-adapter-v3-markdown-run-005
```

Inspector Instruction Qwen3-8B LoRA Adapter v3 Markdown Run 005

This repository contains a PEFT LoRA adapter for unsloth/Qwen3-8B-unsloth-bnb-4bit. The adapter was trained for question answering over the Markdown corpus derived from "Инструкция осмотрщику вагонов о колесных парах, буксовых узлах и тележках железнодорожных вагонов".

Intended Use

The adapter is intended for qualitative analysis, controlled evaluation, and demonstration in the companion Gradio Space:

Fermi37/railway-qwen3-comparison

The model should be used with the base model listed above and the included tokenizer artifacts.

Training Summary

Method: QLoRA SFT
Base model: unsloth/Qwen3-8B-unsloth-bnb-4bit
LoRA rank: 32
LoRA alpha: 64
LoRA dropout: 0.0
Max sequence length: 4096
Prompt format: Qwen3 chat template
Thinking mode: disabled

Evaluation Context

The associated repository evaluation used a 75-question Markdown-grounded corpus from the inspector instruction source. The target run 20260610_194056 reported exact automatic matching against the generated reference answers on source, numeric, token F1, and character-similarity metrics. These automatic metrics support regression checking and require expert review before operational use.

Limitations

This adapter reflects the scope and quality of its training corpus. It should not be used for safety-critical railway operations without independent validation, expert review, and deployment-specific controls.

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