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README.md

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+---
+license: apache-2.0
+language:
+  - ar
+  - en
+pipeline_tag: text-generation
+tags:
+  - text-generation
+  - pytorch
+  - transformers
+  - vllm
+  - causal-lm
+  - depth-extension
+  - arabic
+  - english
+  - karnak
+  - qwen
+base_model: Qwen/Qwen3-30B-A3B-Instruct-2507
+model_name: Karnak
+parameters: 40B
+inference: false
+---
+
+# Karnak: Enhanced Arabic–English Large Language Model
+
+## Model Summary
+
+**Karnak** is a depth-extended causal language model optimized for **Arabic and English** generation. It is built on top of **Qwen/Qwen3-30B-A3B-Instruct-2507**, featuring architectural depth extension and a tokenizer specifically optimized for Arabic to improve fluency and efficiency.
+
+Karnak was trained using **high-quality, filtered data** through a rigorous pipeline to enhance overall instruction-following capabilities, factuality, and robustness.
+
+## Key Features
+
+- **Depth Extension (~40B):** Expanded depth to increase reasoning capacity and improve long-range dependency modeling.
+- **Arabic-Optimized Tokenizer:** Improved Arabic tokenization efficiency, resulting in reduced token fragmentation and higher-quality generation.
+- **Multi-Stage Training:** The model evolved through: Pre-trained weights → Depth Extension → Continued Pre-training → SFT (Supervised Fine-Tuning).
+- **Extended Context Window:** Designed for long-context usage with a **safe context range up to 20K tokens** (recommended to stay within this limit for optimal stability).
+
+## Model Details
+
+- **Model Name:** Karnak
+- **Base Model:** Qwen/Qwen3-30B-A3B-Instruct-2507
+- **Parameter Count:** ~40B (Depth-Extended)
+- **Languages:** Arabic, English
+- **Training:** High-quality filtered data + Multi-stage pipeline (Continued pre-training + SFT)
+- **Safe Context Range:** Up to **20,000 tokens**
+
+---
+
+## Usage
+
+### 1) Hugging Face Transformers
+
+To use Karnak with the standard Transformers library, ensure you have the latest version installed.
+
+```bash
+pip install -U "transformers>=4.40.0" torch accelerate
+```
+
+Python Code Example (Chat Template):
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Applied-Innovation-Center/Karnak"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+)
+
+# Prepare Input
+prompt = "اشرح لي نظرية النسبية بشكل مبسط."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt},
+]
+
+# Apply chat template
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# Generate
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.9,
+)
+
+# Decode output (removing the prompt tokens)
+generated_ids = generated_ids[:, model_inputs.input_ids.shape[1]:]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+2) vLLM (Recommended for Production)
+Karnak is compatible with vLLM for high-throughput inference.
+
+Installation:
+
+```bash
+
+pip install -U vllm
+```
+
+Offline Inference:
+
+```python
+
+from vllm import LLM, SamplingParams
+
+model_id = "Applied-Innovation-Center/Karnak"
+
+# Initialize the model
+llm = LLM(
+    model=model_id,
+    trust_remote_code=True,
+    max_model_len=20000,  # Safe context range
+    tensor_parallel_size=1,  # Adjust based on available GPUs
+)
+
+# Set sampling parameters
+sampling_params = SamplingParams(
+    temperature=0.7,
+    top_p=0.9,
+    max_tokens=512,
+)
+
+# Generate
+prompts = ["ما هي عاصمة مصر؟"]
+outputs = llm.generate(prompts, sampling_params)
+
+for o in outputs:
+    print(f"Prompt: {o.prompt}")
+    print(f"Generated: {o.outputs[0].text}")
+```
+
+Server Mode (OpenAI-Compatible API):
+
+You can serve the model as an API compatible with OpenAI clients:
+
+```bash
+
+vllm serve "Applied-Innovation-Center/Karnak" \
+  --trust-remote-code \
+  --dtype bfloat16 \
+  --port 8000
+```
+
+Citation
+If you use this model in your research or application, please cite it as follows:
+
+```bibtex
+@misc{karnak-40b,
+  title={Karnak: A Depth-Extended Arabic-English LLM},
+  year={2026},
+  publisher={Applied Innovation Center},
+  howpublished={\url{[https://huggingface.co/Applied-Innovation-Center/Karnak](https://huggingface.co/Applied-Innovation-Center/Karnak)}}
+}
+```

chat_template.jinja

@@ -0,0 +1,54 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# 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' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) 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' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "decoder_sparse_step": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 48,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 64,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.001,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 192728
+}

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special_tokens_map.json

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+    "lstrip": false,
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+  }
+}

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+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}