update readme

README.md

@@ -9,13 +9,32 @@ tags:
 - transformers
 - trl
 - unsloth
+license: apache-2.0
+language:
+- ms
+---
+```markdown
+---
+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 Model ID
+# 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
 
@@ -23,23 +42,23 @@ tags:
 
 <!-- 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`
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
+### Model Sources
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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
 
@@ -49,37 +68,73 @@ tags:
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+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
 
-### Downstream Use [optional]
+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
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-[More Information Needed]
+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
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+- 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
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+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
 
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further 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.
 
-[More Information Needed]
+See the `Direct Use` section above for inference. To merge the adapter into a full model:
+
+```python
+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
 
@@ -87,26 +142,43 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
+- **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
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-#### Preprocessing [optional]
-
-[More Information Needed]
+#### 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:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **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
 
@@ -116,35 +188,34 @@ Use the code below to get started with the model.
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+A held‑out set of 100 Malaysian prompts (not seen during training) was used for qualitative evaluation.
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+Performance was assessed on factual recall of personal branding (AtlasFlux, Rainspeed Labs, Muhammad Nabil), colloquial slang, and dialect responses.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Qualitative assessment only (no quantitative benchmarks due to resource constraints).
 
 ### Results
 
-[More Information Needed]
+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
 
-#### Summary
+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 [optional]
+## Model Examination
 
 <!-- Relevant interpretability work for the model goes here -->
 
-[More Information Needed]
+Not performed.
 
 ## Environmental Impact
 
@@ -152,59 +223,75 @@ Use the code below to get started with the model.
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **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 [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+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
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- GPU: NVIDIA T4 (16 GB VRAM)
+- RAM: 12 GB (Colab runtime)
+- Disk: ~78 GB temporary storage
 
 #### Software
 
-[More Information Needed]
+- 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 [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
 **BibTeX:**
 
-[More Information Needed]
+```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:**
 
-[More Information Needed]
+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 [optional]
+## Glossary
 
 <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
 [More Information Needed]
 
-## More Information [optional]
+## More Information
 
 [More Information Needed]
 
-## Model Card Authors [optional]
+## Model Card Authors
 
-[More Information Needed]
+Muhammad Nabil (Rainspeed Labs / AtlasFlux AI)
 
 ## Model Card Contact
 
-[More Information Needed]
+support.atlasflux@gmail.com or via ticket system at [ai.atlasflux.my](https://ai.atlasflux.my)
+
 ### Framework versions
 
-- PEFT 0.19.1
+- PEFT 0.19.1
+```