Instructions to use rainspeed/atlasflux-qwen-7b-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rainspeed/atlasflux-qwen-7b-1.0 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-7b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "rainspeed/atlasflux-qwen-7b-1.0")

Transformers

How to use rainspeed/atlasflux-qwen-7b-1.0 with Transformers:

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

pipe = pipeline("text-generation", model="rainspeed/atlasflux-qwen-7b-1.0")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rainspeed/atlasflux-qwen-7b-1.0", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rainspeed/atlasflux-qwen-7b-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rainspeed/atlasflux-qwen-7b-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rainspeed/atlasflux-qwen-7b-1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rainspeed/atlasflux-qwen-7b-1.0

SGLang

How to use rainspeed/atlasflux-qwen-7b-1.0 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 "rainspeed/atlasflux-qwen-7b-1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rainspeed/atlasflux-qwen-7b-1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "rainspeed/atlasflux-qwen-7b-1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rainspeed/atlasflux-qwen-7b-1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use rainspeed/atlasflux-qwen-7b-1.0 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 rainspeed/atlasflux-qwen-7b-1.0 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 rainspeed/atlasflux-qwen-7b-1.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rainspeed/atlasflux-qwen-7b-1.0 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="rainspeed/atlasflux-qwen-7b-1.0",
    max_seq_length=2048,
)

Docker Model Runner
How to use rainspeed/atlasflux-qwen-7b-1.0 with Docker Model Runner:
```
docker model run hf.co/rainspeed/atlasflux-qwen-7b-1.0
```

---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/qwen2.5-7b-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
- malaysian
- bahasa-melayu
- atlasflux
---

# Model Card for AtlasFlux Qwen 2.5 7B – LoRA Adapter for Malaysian Context

<!-- Provide a quick summary of what the model is/does. -->

This is a **LoRA adapter** fine-tuned on `unsloth/qwen2.5-7b-unsloth-bnb-4bit` to improve understanding of Malaysian Bahasa Melayu, colloquial slang (Manglish), and regional dialects (Kelantan, Kedah, Terengganu, Johor, Sabah, Sarawak). The adapter is designed for chatbots and AI applications serving Malaysian users.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

The model was fine-tuned using **QLoRA (4-bit quantisation)** on a custom instruction‑response dataset of **2,968 examples** covering general knowledge, local culture, everyday conversations, and the personal branding of AtlasFlux AI and Rainspeed Labs. The base model is `unsloth/qwen2.5-7b-unsloth-bnb-4bit`, an optimised 4‑bit version of Qwen2.5‑7B. Training was done on a single Google Colab T4 GPU (16GB VRAM) in about 90 minutes.

- **Developed by:** Muhammad Nabil (Rainspeed Labs / AtlasFlux AI)
- **Funded by:** Self‑funded
- **Shared by:** Muhammad Nabil
- **Model type:** LoRA adapter for decoder‑only transformer
- **Language(s) (NLP):** Bahasa Melayu (Standard, Colloquial, Regional dialects) and English
- **License:** Apache 2.0
- **Finetuned from model:** `unsloth/qwen2.5-7b-unsloth-bnb-4bit`

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [rainspeed/atlasflux-qwen-7b-1.0](https://huggingface.co/rainspeed/atlasflux-qwen-7b-1.0)
- **Paper:** Included as `README.md` and research paper within the repo
- **Demo:** Not yet hosted – see usage instructions

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

Load the adapter with the base model for text generation. **Required prompt format:**

Instruction:\n{user question}\n\n### Response:\n


Example code:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained(
    "unsloth/qwen2.5-7b-unsloth-bnb-4bit",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-7b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base, "rainspeed/atlasflux-qwen-7b-1.0")

prompt = "### Instruction:\nSiapa yang membina AtlasFlux AI?\n\n### Response:\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downstream Use

The adapter can be merged into the base model for full‑weight deployment, or used as‑is with PEFT. Suitable for Malaysian customer support bots, localised Q&A, and educational tools.

Out-of-Scope Use

Generating harmful, discriminatory, or illegal content
High‑stakes decisions (medical, legal, financial) without human verification
Unauthorised commercial use that violates Apache 2.0

Bias, Risks, and Limitations

The model was fine‑tuned on a small dataset (2,968 examples), which may cause limited coverage of certain dialects or topics, occasional repetition, factual errors (hallucinations), and biases present in the training data (e.g., from public forums). No explicit safety alignment was performed.

Recommendations

Users should always validate important outputs before acting on them. For production systems, consider augmenting with retrieval‑augmented generation (RAG) to ground answers in trusted sources. Periodically evaluate model outputs on representative Malaysian user inputs.

How to Get Started with the Model

Use the code below to get started with the model.

See the Direct Use section above for inference. To merge the adapter into a full model:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-7b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base, "rainspeed/atlasflux-qwen-7b-1.0")
merged = model.merge_and_unload()
merged.save_pretrained("atlasflux_merged")

Training Details

Training Data

Size: 2,968 instruction‑response pairs
Sources: Public online forums, social media (Twitter, Facebook public pages, Lowyat.net), synthetic generation using Qwen2.5‑3B
Composition: ~70% Standard Bahasa Melayu, ~20% Colloquial slang (Manglish), ~10% Regional dialects (Kelantan, Kedah, Terengganu, Johor, Sabah, Sarawak)
Format: JSONL with fields instruction and response
Preprocessing: Manual cleaning to remove personally identifiable information (PII) and noise

Training Procedure

Preprocessing

Prompts were formatted as ### Instruction:\n{instruction}\n\n### Response:\n{response}. The dataset was split into training only; no validation split was used.

Training Hyperparameters

Training regime: fp16 mixed precision
LoRA rank (r): 16
LoRA alpha: 16
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Dropout: 0.0
Bias: none
Gradient checkpointing: enabled (Unsloth)
Per‑device batch size: 1
Gradient accumulation steps: 8
Learning rate: 2e‑4
Optimiser: AdamW 8‑bit
Warmup steps: 5
Max steps: 500
Random seed: 3407

Speeds, Sizes, Times

Trainable parameters: ~20.2 million (0.26% of full model)
Training time: ~90 minutes on Google Colab T4 GPU
Adapter size: ~80 MB (safetensors)
Full merged model size: ~15 GB (FP16)

Evaluation

Testing Data, Factors & Metrics

Testing Data

A held‑out set of 100 Malaysian prompts (not seen during training) was used for qualitative evaluation.

Factors

Performance was assessed on factual recall of personal branding (AtlasFlux, Rainspeed Labs, Muhammad Nabil), colloquial slang, and dialect responses.

Metrics

Qualitative assessment only (no quantitative benchmarks due to resource constraints).

Results

The model correctly answered:

“Siapa yang membina AtlasFlux AI?” → returns factual answer with name, UiTM, course
“Aku nak gi mana?” (Kelantan dialect) → appropriate dialect response
“Line internet aku slow gila.” → helpful troubleshooting advice

Limitations: small dataset leads to occasional generic English answers for very rare dialect phrases.

Summary

The fine‑tuned adapter successfully improves Malaysian cultural and linguistic understanding over the base Qwen2.5‑7B model, within the scope of a small‑scale fine‑tuning project.

Model Examination

Not performed.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: NVIDIA T4
Hours used: 1.5 hours
Cloud Provider: Google Colab (assumed US‑central region)
Compute Region: US‑central (estimated)
Carbon Emitted: ~0.06 kg CO₂ (approx.)

Technical Specifications

Model Architecture and Objective

Base architecture: Qwen 2.5‑7B (28 layers, 7.6B parameters). LoRA matrices injected into Q, K, V, O, and MLP projection layers. Training objective: next‑token prediction (causal language modelling).

Compute Infrastructure

Hardware

GPU: NVIDIA T4 (16 GB VRAM)
RAM: 12 GB (Colab runtime)
Disk: ~78 GB temporary storage

Software

Python 3.12
PyTorch 2.1.0
Transformers 4.36.0
PEFT 0.12.0
Unsloth 2026.5.9
bitsandbytes 0.49.2
accelerate 0.25.0

Citation

BibTeX:

@misc{atlasflux2026,
  author       = {Muhammad Nabil},
  title        = {AtlasFlux Qwen 2.5 7B – LoRA adapter for Malaysian cultural and linguistic contexts},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/rainspeed/atlasflux-qwen-7b-1.0}
}

APA:

Muhammad Nabil. (2026). AtlasFlux Qwen 2.5 7B – LoRA adapter for Malaysian contexts. Hugging Face. https://huggingface.co/rainspeed/atlasflux-qwen-7b-1.0

Glossary

[More Information Needed]

More Information

[More Information Needed]

Model Card Authors

Muhammad Nabil (Rainspeed Labs / AtlasFlux AI)

Model Card Contact

support.atlasflux@gmail.com or via ticket system at ai.atlasflux.my

Framework versions

PEFT 0.19.1

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Paper for rainspeed/atlasflux-qwen-7b-1.0

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 52