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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,208 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen3-8B
+language:
+  - af  # Afrikaans
+  - am  # Amharic
+  - ar  # Arabic
+  - en  # English
+  - fr  # French
+  - ha  # Hausa
+  - ig  # Igbo
+  - mg  # Malagasy (Plateau)
+  - ny  # Nyanja
+  - om  # Oromo
+  - pt  # Portuguese
+  - rw  # Kinyarwanda
+  - sn  # Shona
+  - so  # Somali
+  - st  # Southern Sotho
+  - sw  # Swahili
+  - ti  # Tigrinya
+  - tn  # Tswana
+  - xh  # Xhosa
+  - yo  # Yoruba
+  - zu  # Zulu
+pipeline_tag: text-generation
+tags:
+  - african-languages
+  - multilingual
+  - continued-pretraining
+  - afrique-llm
+  - qwen
+---
+
+# AfriqueQwen-8B
+
+<p align="center">
+  <img src="language_group_scores.pdf" alt="AfriqueLLM Evaluation Results" width="600"/>
+</p>
+
+## Model Overview
+
+**AfriqueQwen-8B** is part of the **AfriqueLLM** suite—a collection of open language models adapted to **20 African languages** through continued pre-training (CPT) on **26B tokens**. This model is based on [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) and has been specifically adapted for improved performance on African languages while maintaining strong capabilities in high-resource languages.
+
+Our experiments show that **Qwen 3 models achieve the best performance** among all base models tested, better preserving performance in high-resource languages after CPT and achieving strong results on long-context tasks such as document-level translation.
+
+### Key Features
+
+- **Type**: Causal Language Model (Base/Pre-trained)
+- **Base Model**: Qwen 3 8B
+- **Parameters**: 8B
+- **Context Length**: 32,768 tokens (native)
+- **Training Tokens**: 26B tokens of carefully curated multilingual data
+
+## Supported Languages
+
+AfriqueQwen-8B has been adapted for the following 20 African languages plus 4 high-resource languages:
+
+| Language | Code | Family | Script |
+|----------|------|--------|--------|
+| Afrikaans | afr_Latn | Germanic | Latin |
+| Swahili | swh_Latn | Bantu | Latin |
+| Moroccan Arabic | ary_Arab | Semitic | Arabic |
+| Somali | som_Latn | Cushitic | Latin |
+| Amharic | amh_Ethi | Semitic | Ethiopic |
+| Egyptian Arabic | arz_Arab | Semitic | Arabic |
+| Hausa | hau_Latn | Chadic | Latin |
+| Kinyarwanda | kin_Latn | Bantu | Latin |
+| Zulu | zul_Latn | Bantu | Latin |
+| Igbo | ibo_Latn | Volta-Niger | Latin |
+| Plateau Malagasy | plt_Latn | Austronesian | Latin |
+| Xhosa | xho_Latn | Bantu | Latin |
+| Shona | sna_Latn | Bantu | Latin |
+| Yoruba | yor_Latn | Volta-Niger | Latin |
+| Nyanja | nya_Latn | Bantu | Latin |
+| Southern Sotho | sot_Latn | Bantu | Latin |
+| Tigrinya | tir_Ethi | Semitic | Ethiopic |
+| Tunisian Arabic | aeb_Arab | Semitic | Arabic |
+| Oromo | gaz_Latn | Cushitic | Latin |
+| Tswana | tsn_Latn | Bantu | Latin |
+
+**High-resource languages (for catastrophic forgetting mitigation):** English, French, Portuguese, Arabic
+
+## Training Data
+
+Our training corpus combines multiple high-quality sources:
+
+- **African Monolingual Data** (~22.8B tokens): FineWeb2, WURA, and MADLAD-400
+- **Code** (~1B tokens): CornStack-Python for reasoning capabilities
+- **Mathematics** (~1B tokens): FineMath-4+ for mathematical understanding
+- **Synthetic Data** (~324M tokens): GPT-4.1 translated domain-specific content across 10 domains
+- **Parallel Data** (~456M tokens): NLLB-OPUS filtered with SSA-COMET (threshold 0.7)
+
+We use **UniMax sampling** to create a balanced distribution, capping high-resource languages at approximately 1B tokens and upsampling lower-resource languages for up to five epochs.
+
+## Quickstart
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "McGill-NLP/AfriqueQwen-8B"
+
+# Load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# Prepare the model input
+prompt = "Bawo ni o ṣe n ṣe?"  # Yoruba: "How are you doing?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# Generate text
+generated_ids = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9
+)
+output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+print(output)
+```
+
+## Deployment
+
+For deployment, you can use `vllm` or `sglang` to create an OpenAI-compatible API endpoint:
+
+**vLLM:**
+```shell
+vllm serve McGill-NLP/AfriqueQwen-8B
+```
+
+**SGLang:**
+```shell
+python -m sglang.launch_server --model-path McGill-NLP/AfriqueQwen-8B
+```
+
+## Training Details
+
+### Hyperparameters
+
+- **Learning Rate**: 5e-5 (with warmup and cosine decay)
+- **Context Length**: 8,192 tokens (training)
+- **Batch Size**: Effective batch size optimized for throughput
+- **Optimizer**: AdamW
+- **Precision**: BF16 mixed precision
+
+### Infrastructure
+
+Training was conducted using the LLaMA-Factory framework on up to 64 NVIDIA H100 GPUs with:
+- DeepSpeed ZeRO-1/ZeRO-2
+- Flash Attention 3
+- Sequence packing
+- Liger Kernel optimizations
+
+## Evaluation
+
+AfriqueQwen-8B is evaluated on multiple multilingual benchmarks including:
+
+- **AfriMGSM**: Mathematical reasoning
+- **AfriMMLU**: Multilingual knowledge
+- **AfriXNLI**: Natural language inference
+- **Belebele**: Reading comprehension
+- **SIB-200**: Topic classification
+- **FLORES**: Machine translation
+
+## Model Variants
+
+- [AfriqueQwen-14B](https://huggingface.co/McGill-NLP/AfriqueQwen-14B) - Larger, more capable variant
+
+## Intended Use
+
+This model is designed for:
+- Research on African language NLP
+- Building applications for African language communities
+- Cross-lingual transfer learning experiments
+- Multilingual text generation and understanding
+
+## Limitations
+
+- This is a **base/pre-trained model** and may require fine-tuning for specific tasks
+- Performance varies across languages based on data availability
+- May generate biased or inappropriate content without proper safeguards
+- Not suitable for production use without additional safety measures
+
+## Citation
+
+If you find our work helpful, please cite:
+
+```bibtex
+@article{afriquellm2025,
+  title={AfriqueLLM: How Data Mixing and Model Architecture Impact Continued Pre-training for African Languages},
+  author={AfriqueLLM Team},
+  year={2025}
+}
+```
+
+## License
+
+This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). Please review the license terms before use.
+
+## Acknowledgments
+
+We thank the creators of the base models and datasets that made this work possible, including Alibaba (Qwen), the FineWeb team, WURA, MADLAD-400, and the NLLB project.

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
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+  "<|object_ref_end|>": 151647,
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+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.4",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.55.4"
+}

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