Instructions to use FHJibon/Bangla-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FHJibon/Bangla-LLM with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-7B-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "FHJibon/Bangla-LLM")

Transformers

How to use FHJibon/Bangla-LLM with Transformers:

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

pipe = pipeline("text-generation", model="FHJibon/Bangla-LLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("FHJibon/Bangla-LLM", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FHJibon/Bangla-LLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FHJibon/Bangla-LLM

SGLang

How to use FHJibon/Bangla-LLM 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 "FHJibon/Bangla-LLM" \
    --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": "FHJibon/Bangla-LLM",
		"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 "FHJibon/Bangla-LLM" \
        --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": "FHJibon/Bangla-LLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FHJibon/Bangla-LLM with Docker Model Runner:
```
docker model run hf.co/FHJibon/Bangla-LLM
```

Bangla-LLM / README.md

FHJibon

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metadata

base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
library_name: peft
pipeline_tag: text-generation
language:
  - bn
  - en
tags:
  - base_model:adapter:unsloth/Qwen2.5-7B-Instruct-bnb-4bit
  - lora
  - sft
  - transformers
  - trl
  - bangla
  - customer-support
license: mit

🇧🇩 BanglaSupport-LLM: Fine-Tuned Bangla Customer Support Model

BanglaSupport-LLM is a domain-adapted, fine-tuned Large Language Model optimized specifically for Bangla E-Commerce Customer Support. Fine-tuned from Qwen2.5-7B-Instruct using Unsloth QLoRA, this model eliminates cross-lingual Hindi-bleeding, offering natural, professional, and grammatically accurate customer support responses in native Bengali.

📌 Model Details

Developed by: Mahmudur Rahman (mrshibly) & Ferdous Hasan (FHJibon)
Model Type: PEFT / LoRA Adapter for Causal Language Modeling
Language(s): Bengali (bn), English (en)
License: MIT
Finetuned from model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit

🔗 Model Sources & Links

GitHub Repository: github.com/FHJibon/BanglaLLM
Hugging Face Model Hub: huggingface.co/FHJibon/Bangla-LLM

Uses

Direct Use

E-commerce customer service automation in Bangla.
Answering queries regarding order tracking, shipping, return policies, payment options, and refund eligibility.
Multi-turn conversational support with system persona framing.

Out-of-Scope Use

Medical, legal, or high-risk financial advice.
Generating non-Bengali support text where strict monolingual output is expected.

How to Get Started with the Model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE_MODEL = "unsloth/Qwen2.5-7B-Instruct-bnb-4bit"
ADAPTER_ID = "mrshibly/bangla-support-qwen3-8b"

print("Loading model and tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    device_map="auto",
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(base_model, ADAPTER_ID)
model.eval()

system_prompt = "তুমি একজন সহায়ক বাংলা ই-কমার্স গ্রাহক সেবা সহকারী।"
user_question = "আমার অর্ডারটি ৩ দিন ধরে পেন্ডিং আছে, ডেলিভারি কখন পাব?"

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_question},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)

print("Response:", response)

Training Details

Training Data

Trained on a curated dataset of 25,000 normalized Bangla instruction pairs filtered from:

md-nishat-008/Bangla-Instruct (ACL 2025 benchmark dataset)
CohereForAI/aya_dataset (Bengali subset)

Dataset preprocessing included NFC Unicode normalization, MinHash LSH deduplication, and instruction-intent filtering.

Training Procedure

Framework: PyTorch 2.11.0 + Unsloth FastLanguageModel + SFTTrainer
Method: QLoRA 4-bit (NormalFloat4 quantization)
Precision: bfloat16
LoRA Parameters: $r = 16$, $\alpha = 32$, Dropout = 0.0
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Evaluation Results

Evaluated against held-out test data using automated metrics & LLM-as-a-Judge benchmarking:

Model Variant	BLEU-4	ROUGE-L	BERTScore (F1)	LLM-Judge (Fluency)	LLM-Judge (Accuracy)
Base Qwen2.5-7B-Instruct	0.1820	0.3840	0.7620	3.4 / 5.0	3.1 / 5.0
Fine-Tuned BanglaSupport-LLM	0.4280	0.6910	0.9140	4.8 / 5.0	4.7 / 5.0

BERTScore evaluated using sagorsarker/bangla-bert-base.

Hardware & Compute

Hardware: NVIDIA GeForce RTX 5060 Ti (16GB VRAM)
Platform: Windows / CUDA 12.0
Framework Versions: PEFT 0.19.1, Transformers 5.5.0, Unsloth 2026.7.3

Authors & Contact

Mahmudur Rahman (mrshibly)
- GitHub: @mrshibly
FHJibon
- GitHub: @FHJibon