🇨🇬 Llama-3.1-8B-Instruct-Lingala-QLoRA-merged-v2
A Meta-Llama-3.1-8B-Instruct model adapted to Lingala through supervised fine-tuning (QLoRA/LoRA), developed by Congo Digital Services (CDS). This model is intended for conversation, text generation, summarization, translation, and classification in Lingala.
Model Details
Model Description
- Developed by: Congo Digital Services (CDS SARL) — congo-digital.com
- Base model: meta-llama/Meta-Llama-3.1-8B-Instruct
- Adaptation method: Supervised fine-tuning via QLoRA/LoRA
- Language: Lingala (ln)
- Model type: Causal language model, 8B parameters, merged
- License: Llama 3.1 Community License
- Functional objectives: Conversation, text generation, summarization, translation, classification, and conversational responses tailored to Lingala
Model Sources
Uses
Direct Use
This model can be used directly for conversational text generation in Lingala via the standard transformers API, or deployed behind a compatible inference server (see Deployment Infrastructure below).
Out-of-Scope Use
This model is not intended for use cases requiring critical factual accuracy (medical, legal domains) without human oversight, nor for certified professional translation tasks.
How to Get Started with the Model
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_id = "Congo-digital-service/Llama-3.1-8B-Instruct-Lingala-QLoRA-merged-v2"
4
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
7
8messages = [
9 {"role": "user", "content": "Loba na ngai na lingala : ndenge nini okoki kosala mombongo na Kinshasa ?"}
10]
11
12inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
13outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
14print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
Training Data
A unified corpus of 4,818 examples across five stylistic categories:
| Category | Examples |
|---|
| Urban | 1,425 |
| Educational | 1,063 |
| Summarization | 1,041 |
| Formal | 889 |
| Translation | 400 |
Split: stratified 80/20 split → 3,854 initial training examples / 964 test examples (unaugmented, held out for independent evaluation).
Augmentation: applied only to the training set to balance categories at 1,140 examples each, resulting in a final training set of 5,700 examples.
Training Procedure
- Environment: Google Colab, with checkpoints and run logs saved to Google Drive
Training Hyperparameters
| Parameter | Value |
|---|
| Epochs | 3 |
| Batch size | 2 |
| Gradient accumulation steps | 8 (effective batch = 16) |
| Initial learning rate | 2 × 10⁻⁵ |
| Scheduler | Cosine |
| Intermediate evaluations | Every 200 steps |
Training Convergence
| Step | Training Loss | Validation Loss | Entropy | Mean Token Accuracy |
|---|
| 200 | 1.5638 | 1.5747 | 1.6141 | 67.15% |
| 400 | 1.3008 | 1.3514 | 1.3425 | 69.81% |
| 600 | 1.2714 | 1.2901 | 1.3146 | 70.83% |
| 800 | 1.2072 | 1.2741 | 1.2669 | 71.14% |
| 1000 | 1.2211 | 1.2720 | 1.2690 | 71.15% |
| 1071 | 1.1956 | 1.2720 | 1.2689 | 71.19% |
Validation loss decreases steadily throughout training with no sign of overfitting.
Evaluation
Evaluation performed on the independent test set (964 examples), held out during training.
Final Results
| Metric | Base Model | Fine-tuned Model | Improvement |
|---|
| ROUGE-L F1 (primary metric) | 0.1521 | 0.2379 | +56.4% |
| Mean Token Accuracy | — | 71.20% | — |
| Token F1 (lexical) | 0.1721 | — | — |
| Perplexity | — | 17.7211 | — |
| ROUGE-1 | — | 0.2939 | — |
| ROUGE-2 | — | 0.0789 | — |
| BLEU (SacreBLEU for base) | 2.3280 | 0.0459 | not representative* |
| Exact match | 0.00% | not computed | — |
* BLEU proves unrepresentative for open-ended generation tasks; progress is primarily measured via ROUGE-L.
Summary
The QLoRA adaptation delivers a robust, measurable performance gain: the fine-tuned model handles Lingala's linguistic structures significantly better than the base model, with a +56.4% relative gain in ROUGE-L F1.
Human Evaluation
- Operational target: ≥ 85% of responses rated acceptable (per the project's Terms of Reference)
- Dimensions evaluated: Lingala correctness, coherence and meaning, instruction adherence, business relevance
Target Deployment Infrastructure
| Component | Detail |
|---|
| Inference server | vLLM deployed on GPU pods |
| Gateway / auth | LiteLLM v1.87.0 + PostgreSQL 16 |
| Endpoint | POST ${VLLM_BASE_URL}/v1/chat/completions |
| Format | OpenAI Chat Completions compatible (choices[0].message.content) |
| Access interface | LoBAI (LibreChat-based) with Keycloak / OpenID Connect |
Traceability
- Merged model:
Congo-digital-service/Llama-3.1-8B-Instruct-Lingala-QLoRA-merged-v2
- QLoRA adapters:
Congo-digital-service/Llama-3.1-8B-Instruct-Lingala-QLoRA-adapters
- Version manifest and checksums (SHA-256): centralized in the project's delivery GitHub repository
Bias, Risks, and Limitations
This model was trained on a moderately sized corpus (5,700 augmented examples) covering five stylistic registers. Performance may vary outside these registers, particularly on regional Lingala dialects not represented in the corpus, or on specialized technical domains absent from the training set. Users should validate model outputs before any high-stakes use.
Environmental Impact
Training was performed on Google Colab infrastructure. Carbon emissions can be estimated using the
ML Impact calculator (
Lacoste et al., 2019).
Citation
BibTeX:
1@misc{cds2026lingala,
2 title={Llama-3.1-8B-Instruct-Lingala-QLoRA},
3 author={Congo Digital Services},
4 year={2026},
5 howpublished={\url{https://huggingface.co/Congo-digital-service/Llama-3.1-8B-Instruct-Lingala-QLoRA-merged-v2}}
6}
Contact
- Organization: Congo Digital Services (CDS SARL)
- Website: congo-digital.com
- Email: info@congo-digital.com
