Instructions to use rohan-bansode/Qwen-2.5-3B-QLORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rohan-bansode/Qwen-2.5-3B-QLORA with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("rohan-bansode/Qwen-2.5-3B-QLORA")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Transformers

How to use rohan-bansode/Qwen-2.5-3B-QLORA with Transformers:

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

pipe = pipeline("text-generation", model="rohan-bansode/Qwen-2.5-3B-QLORA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rohan-bansode/Qwen-2.5-3B-QLORA")
model = AutoModelForCausalLM.from_pretrained("rohan-bansode/Qwen-2.5-3B-QLORA", 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
LM Studio

vLLM

How to use rohan-bansode/Qwen-2.5-3B-QLORA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rohan-bansode/Qwen-2.5-3B-QLORA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rohan-bansode/Qwen-2.5-3B-QLORA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rohan-bansode/Qwen-2.5-3B-QLORA

SGLang

How to use rohan-bansode/Qwen-2.5-3B-QLORA 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 "rohan-bansode/Qwen-2.5-3B-QLORA" \
    --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": "rohan-bansode/Qwen-2.5-3B-QLORA",
		"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 "rohan-bansode/Qwen-2.5-3B-QLORA" \
        --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": "rohan-bansode/Qwen-2.5-3B-QLORA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use rohan-bansode/Qwen-2.5-3B-QLORA with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rohan-bansode/Qwen-2.5-3B-QLORA"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rohan-bansode/Qwen-2.5-3B-QLORA"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use rohan-bansode/Qwen-2.5-3B-QLORA with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rohan-bansode/Qwen-2.5-3B-QLORA"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "rohan-bansode/Qwen-2.5-3B-QLORA" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use rohan-bansode/Qwen-2.5-3B-QLORA with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "rohan-bansode/Qwen-2.5-3B-QLORA"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "rohan-bansode/Qwen-2.5-3B-QLORA"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "rohan-bansode/Qwen-2.5-3B-QLORA",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use rohan-bansode/Qwen-2.5-3B-QLORA with Docker Model Runner:
```
docker model run hf.co/rohan-bansode/Qwen-2.5-3B-QLORA
```

Hermes Agent

How to use rohan-bansode/Qwen-2.5-3B-QLORA with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rohan-bansode/Qwen-2.5-3B-QLORA"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default rohan-bansode/Qwen-2.5-3B-QLORA

Run Hermes

hermes

Atomic Chat

Qwen-2.5-3B-QLORA

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---
base_model: mlx-community/Qwen2.5-3B-Instruct-4bit
language:
- en
license: apache-2.0
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- finance
- financial-analysis
- mlx
- qlora
- transformers
- qwen
library_name: mlx
datasets:
- FinanceMTEB/financial_phrasebank
- ChanceFocus/flare-finqa
- ChanceFocus/flare-convfinqa
- ChanceFocus/flare-tatqa
- ChanceFocus/flare-headlines
- ChanceFocus/flare-ner
- gbharti/finance-alpaca
---
# Qwen 2.5 3B QLoRA — Financial Reasoning Model

A fine-tuned version of Qwen 2.5 3B specialized in financial calculations
(EBIT, EBITDA, EBITDAR) and multi-step numerical reasoning over structured inputs.

---


## Quick Start

### Install
```bash
pip install mlx-lm
```

### Run Inference
```python
from mlx_lm import load, stream_generate

model, tokenizer = load("rohan-bansode/Qwen-2.5-3B-QLORA")

prompt = """
Analyze:
Revenue = 500k
Operating Expenses = 350k
Rent = 50k

Calculate EBITDAR and explain.
"""

stop_sequences = ["<|endoftext|>", "<|im_end|>", "Human:", "Assistant:"]

print("--- Financial Sandbox Output ---")

for response in stream_generate(model, tokenizer, prompt, max_tokens=512):
    if any(stop in response.text for stop in stop_sequences):
        break
    print(response.text, end="", flush=True)
```
## Example Output

```text
EBITDAR = 200k

Explanation:
EBITDAR = Revenue - Operating Expenses + Rent
= 500k - 350k + 50k = 200k
```

## What this model is good at

- Financial reasoning (EBIT, EBITDA, EBITDAR)
- Multi-step numerical calculations
- Structured financial Q&A

## Limitations

- Not reliable for real financial decisions
- Can make calculation mistakes if prompt is unclear
- Limited context (~1.5k tokens)

## Improvements over base model

- More consistent financial calculations
- Better step-by-step reasoning for numerical tasks
- Reduced hallucination in structured financial prompts