Instructions to use afifaimran/meno_llm_3b_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afifaimran/meno_llm_3b_v2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("hishab/titulm-llama-3.2-3b-v2.0")
model = PeftModel.from_pretrained(base_model, "afifaimran/meno_llm_3b_v2")

Transformers

How to use afifaimran/meno_llm_3b_v2 with Transformers:

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

pipe = pipeline("text-generation", model="afifaimran/meno_llm_3b_v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("afifaimran/meno_llm_3b_v2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use afifaimran/meno_llm_3b_v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afifaimran/meno_llm_3b_v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afifaimran/meno_llm_3b_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/afifaimran/meno_llm_3b_v2

SGLang

How to use afifaimran/meno_llm_3b_v2 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 "afifaimran/meno_llm_3b_v2" \
    --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": "afifaimran/meno_llm_3b_v2",
		"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 "afifaimran/meno_llm_3b_v2" \
        --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": "afifaimran/meno_llm_3b_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use afifaimran/meno_llm_3b_v2 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 afifaimran/meno_llm_3b_v2 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 afifaimran/meno_llm_3b_v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for afifaimran/meno_llm_3b_v2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="afifaimran/meno_llm_3b_v2",
    max_seq_length=2048,
)

Docker Model Runner
How to use afifaimran/meno_llm_3b_v2 with Docker Model Runner:
```
docker model run hf.co/afifaimran/meno_llm_3b_v2
```

meno_llm_3b_v2

This repository contains a LoRA adapter fine-tuned on top of:

Base model: hishab/titulm-llama-3.2-3b-v2.0

Description

This model is being developed as a Bangla empathetic conversational assistant for health-related question answering.

It is intended to be used in a retrieval-augmented generation (RAG) pipeline, where relevant context is retrieved first and then passed to the model for response generation.

Important Note

This repository contains only the LoRA adapter, not the full standalone base model.

To use it, load it on top of:

hishab/titulm-llama-3.2-3b-v2.0

Current Status

Fine-tuned adapter uploaded successfully
Produces empathetic Bangla answers
Works better when used with retrieved supporting context
Still under improvement for grounding, medical faithfulness, and weak-context handling

Intended Use

Bangla conversational response generation
Empathetic health-related QA
RAG-based assistant workflows
Research and development use

Limitations

This model is still under active development
It may produce incorrect or weakly grounded answers if retrieval quality is poor
It should not be treated as a substitute for professional medical advice

Loading

Use this adapter together with the base TiTuLM model.

Base model: hishab/titulm-llama-3.2-3b-v2.0

Adapter repo: afifaimran/meno_llm_3b_v2

Author

Afifa Imran

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