Instructions to use Nishant1600/qwen3-1_7b-scl-extractor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nishant1600/qwen3-1_7b-scl-extractor with Transformers:

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

pipe = pipeline("text-generation", model="Nishant1600/qwen3-1_7b-scl-extractor")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nishant1600/qwen3-1_7b-scl-extractor")
model = AutoModelForCausalLM.from_pretrained("Nishant1600/qwen3-1_7b-scl-extractor", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nishant1600/qwen3-1_7b-scl-extractor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nishant1600/qwen3-1_7b-scl-extractor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nishant1600/qwen3-1_7b-scl-extractor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nishant1600/qwen3-1_7b-scl-extractor

SGLang

How to use Nishant1600/qwen3-1_7b-scl-extractor 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 "Nishant1600/qwen3-1_7b-scl-extractor" \
    --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": "Nishant1600/qwen3-1_7b-scl-extractor",
		"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 "Nishant1600/qwen3-1_7b-scl-extractor" \
        --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": "Nishant1600/qwen3-1_7b-scl-extractor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nishant1600/qwen3-1_7b-scl-extractor with Docker Model Runner:
```
docker model run hf.co/Nishant1600/qwen3-1_7b-scl-extractor
```

Qwen3-1.7B SCL Extractor

Fine-tuned Qwen/Qwen3-1.7B (QLoRA, 4-bit NF4, merged to bf16) that extracts structured safety facts from industrial incident narratives as strict JSON.

Trained on ~30k OSHA severe-injury reports (2015–2025).

Output format

The model outputs only a single JSON object:

{
  "energy": {
    "energy_type": "gravity",
    "magnitude": 8.0,
    "unit": "feet",
    "evidence": "fell approximately 8 feet"
  },
  "injury": {
    "injury_degree": "serious",
    "evidence": "fracturing his elbow"
  }
}

energy_type ∈ {gravity, motion_vehicle, electrical, pressure, thermal, chemical, other, not_stated}
injury_degree ∈ {fatal, serious, minor, none, not_stated}
evidence fields are verbatim substrings of the input narrative.
The model never outputs a safety classification — high_energy is decided in code by comparing magnitude/unit against thresholds.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("Nishant1600/qwen3-1_7b-scl-extractor")
model = AutoModelForCausalLM.from_pretrained(
    "Nishant1600/qwen3-1_7b-scl-extractor",
    torch_dtype="auto", device_map="auto",
)

prompt = (
    "<|im_start|>system\n"
    "You are a workplace safety fact extractor... output ONLY a single JSON object.\n"
    "<|im_end|>\n"
    "<|im_start|>user\nNarrative:\n<your narrative here><|im_end|>\n"
    "<|im_start|>assistant\n<think>\n\n</think>\n\n"   # no-think switch
)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False,
                     pad_token_id=tok.eos_token_id)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Notes:

Use greedy decoding (do_sample=False, temperature=0) for deterministic extractions.
The empty <think> block after <|im_start|>assistant\n disables Qwen3 reasoning mode.

Limitations

Injury labels skew toward serious because the OSHA severe-injury dataset contains severe cases only.
English narratives only; trained on US oil/gas/industrial report style.

Intended use

Backend extractor for the SIH26165 safety-compliance pipeline. Not a substitute for professional safety judgment.

Downloads last month: 360

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Nishant1600/qwen3-1_7b-scl-extractor

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Adapter

(648)

this model