Instructions to use dorito96/qwen2.5-1.5b_causal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dorito96/qwen2.5-1.5b_causal with Transformers:

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

pipe = pipeline("text-generation", model="dorito96/qwen2.5-1.5b_causal")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dorito96/qwen2.5-1.5b_causal")
model = AutoModelForCausalLM.from_pretrained("dorito96/qwen2.5-1.5b_causal")
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 dorito96/qwen2.5-1.5b_causal with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dorito96/qwen2.5-1.5b_causal"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dorito96/qwen2.5-1.5b_causal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dorito96/qwen2.5-1.5b_causal

SGLang

How to use dorito96/qwen2.5-1.5b_causal 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 "dorito96/qwen2.5-1.5b_causal" \
    --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": "dorito96/qwen2.5-1.5b_causal",
		"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 "dorito96/qwen2.5-1.5b_causal" \
        --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": "dorito96/qwen2.5-1.5b_causal",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dorito96/qwen2.5-1.5b_causal with Docker Model Runner:
```
docker model run hf.co/dorito96/qwen2.5-1.5b_causal
```

Qwen-1.5B Causal

A derivative of Qwen/Qwen2.5-1.5B fine-tuned to extract causal links of the form
A ->+ B (positive) and A ->- B (negative) from natural-language paragraphs.

License: Derivative of Qwen/Qwen2.5-1.5B. See LICENSE. Users must comply with the base license.

Quickstart

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import re

MODEL = "dorito96/qwen2.5-1.5b_causal"

tok = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL,
    dtype=(torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported()
                 else (torch.float16 if torch.cuda.is_available() else torch.float32)),
    device_map=("auto" if torch.cuda.is_available() else "cpu"),
    trust_remote_code=True,
)
model.eval()

PROMPT_PREFIX = "### Paragraph:\n"
TARGET_PREFIX = "\n\n### Targets:\n"

paragraph = "More rainfall increases crop yield."
prompt = f"{PROMPT_PREFIX}{paragraph}{TARGET_PREFIX}"

inputs = tok(prompt, return_tensors="pt").to(next(model.parameters()).device)
gen = model.generate(
    **inputs,
    max_new_tokens=128,
    num_beams=6,
    do_sample=False,
    eos_token_id=tok.eos_token_id,
    pad_token_id=tok.pad_token_id,
    no_repeat_ngram_size=3,
)
text = tok.decode(gen[0, inputs['input_ids'].shape[1]:], skip_special_tokens=True).strip()
print(text) # rainfall ->+ crop yield

Limitations

Optimized for extracting causal relationships. This is not a general chat model.
May hallucinate on out-of-domain inputs. Usually works best on sentences where causal relationships are explicit (as shown in code example). I will be working on improving this steadily as best as I can.

Acknowledgments

Base model by Qwen team.
© 2025 Aritra Majumdar (GitHub: https://github.com/bear96). Provided for research and educational use with attribution.

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