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Model: enstazao/Qalb-1.0-8B-Instruct Source: Original Platform
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README.md
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README.md
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---
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base_model: unsloth/Meta-Llama-3.1-8B
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library_name: transformers
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tags:
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- unsloth
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- urdu
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- llama-3.1
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- instruct
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- fine-tuned
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- nlp
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license: apache-2.0
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language:
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- ur
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- en
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---
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# Qalb-1.0-8B-Instruct (Urdu Llama 3.1)
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<div align="center">
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</div>
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**Qalb-1.0-8B-Instruct** is a state-of-the-art Urdu language model designed to bridge the gap in low-resource language processing. Built on the powerful **Llama-3.1-8B** architecture, Qalb has been rigorously adapted for the Urdu language through a two-stage process: **Continued Pre-training** on a massive Urdu corpus of 1.97 billion tokens followed by **Supervised Fine-Tuning** for instruction following.
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Unlike general multilingual models that struggle with Urdu grammar and cultural nuance, **Qalb** delivers fluent, culturally accurate, and context-aware responses.
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## 🌟 Key Features
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* **State-of-the-Art Performance:** Outperforms previous best models (Alif-1.0 and LLaMA-3.1 Base) on 6 out of 7 benchmarks.
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* **Deep Urdu Understanding:** Pre-trained on a diverse mix of news, literature, government documents, and social media to capture the depth of the language.
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* **Ethical & Safe:** Fine-tuned to provide helpful, harmless, and honest assistants, refusing to generate toxic or misleading content.
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* **Reasoning Capable:** Excellent performance on logical reasoning, mathematical word problems, and commonsense tasks in Urdu.
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* **Bilingual Proficiency:** Retains strong English capabilities while excelling in Urdu, making it ideal for translation and code-switching tasks.
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## 📊 Performance Benchmarks
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Qalb establishes a new standard for Urdu LLMs, achieving an **Overall Score of 90.34**. It significantly outperforms the base model and the previous state-of-the-art.
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### 🏆 Comparison vs. SOTA Models
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| Task | **Qalb (Ours)** | **Alif-1.0-Instruct** | **LLaMA-3.1-8B-Instruct** |
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| :--- | :---: | :---: | :---: |
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| **Overall Score** | **90.34** | 87.1 | 45.7 |
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| **Translation** | **94.41** | 89.3 | 58.9 |
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| **Classification** | **96.38** | 93.9 | 61.4 |
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| **Sentiment Analysis** | **95.79** | 94.3 | 54.3 |
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| **Ethics** | **90.83** | 85.7 | 27.3 |
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| **Reasoning** | **88.59** | 83.5 | 45.6 |
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| **QA (Question Answering)**| **80.40** | 73.8 | 30.5 |
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| **Generation** | 85.97 | **90.2** | 42.8 |
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*> **Note:** Scores are on a 0-100 scale. Qalb outperforms the previous best model (Alif) in **6 out of 7** categories.*
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## 🚀 How to Use
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### Google COlab
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[](https://colab.research.google.com/drive/1SQ_OaPhr1Q130FDho89zvughfRxJqdoF?usp=sharing)
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### Method 1: Using Unsloth (Recommended - Fast & Efficient)
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The easiest way to run Qalb is using the Unsloth library, which provides 2x faster inference.
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```python
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from unsloth import FastLanguageModel
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import torch
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "enstazao/Qalb-1.0-8B-Instruct",
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max_seq_length = 2048,
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dtype = None,
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load_in_4bit = True, # <--- Currently set to use 4-bit quantization
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)
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FastLanguageModel.for_inference(model)
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urdu_system_prompt = "آپ ایک مددگار اور بے ضرر مصنوعی ذہانت کے اسسٹنٹ ہیں۔ آپ اردو میں سوالات کے درست جوابات دیتے ہیں۔"
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questions = [
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"پاکستان کا قومی کھیل کیا ہے؟",
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"لاہور شہر کیوں مشہور ہے؟ مختصر وضاحت کریں۔",
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"سوال: لیاقت علی خان کون تھے؟",
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"کراچی کو روشنیوں کا شہر کیوں کہا جاتا ہے؟",
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"انگریزی میں ترجمہ کریں: 'محنت کامیابی کی کنجی ہے۔'"
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]
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print("🚀 Starting Batch Generation...\n")
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for user_input in questions:
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print(f"🔹 Question: {user_input}")
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# Manually Format Prompt (Llama-3 Style)
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prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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{urdu_system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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"""
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inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")
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outputs = model.generate(
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**inputs,
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max_new_tokens = 256,
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temperature = 0.1,
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top_p = 0.9,
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repetition_penalty = 1.1,
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do_sample = True,
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eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
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)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
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print(f"✅ Answer: {response}")
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print("-" * 50)
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```
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## Method 2: Using Hugging Face Transformers
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Compatible with standard transformers if Unsloth is not available.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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model_name = "enstazao/Qalb-1.0-8B-Instruct"
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urdu_system_prompt = "آپ ایک مددگار اور بے ضرر مصنوعی ذہانت کے اسسٹنٹ ہیں۔ آپ اردو میں سوالات کے درست جوابات دیتے ہیں۔"
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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)
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print("⏳ Loading model in 4-bit...")
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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quantization_config=bnb_config, # <--- Apply 4-bit here
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device_map="auto" # <--- Required for quantization
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)
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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questions = [
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"پاکستان کا قومی کھیل کیا ہے؟",
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"لاہور شہر کیوں مشہور ہے؟ مختصر وضاحت کریں۔",
|
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"سوال: لیاقت علی خان کون تھے؟",
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"سوال: اسلام آباد شہر کے بارے میں بتائیں۔",
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"انگریزی میں ترجمہ کریں: 'محنت کامیابی کی کنجی ہے۔'"
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]
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print("Model Loaded. Starting Generation...\n")
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# 5. Loop through questions
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for user_input in questions:
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print(f"🔹 Question: {user_input}")
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prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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{urdu_system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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"""
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input_ids = tokenizer([prompt], return_tensors="pt").to("cuda")
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outputs = model.generate(
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**input_ids,
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max_new_tokens = 256,
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temperature = 0.1,
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top_p = 0.9,
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repetition_penalty = 1.1,
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do_sample = True,
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eos_token_id = terminators
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)
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response = tokenizer.decode(outputs[0][input_ids['input_ids'].shape[1]:], skip_special_tokens=True)
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print(f"✅ Answer: {response}")
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print("-" * 50)
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```
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## Limitation & Bias
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While Qalb has been trained to be helpful and harmless, it may still reflect biases present in the training data. Users should fact-check critical information, especially in medical, legal, or religious contexts.
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## Citation
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If you use QALB in your research, please cite:
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```
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@article{qalb2025,
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title={Qalb: Largest State-of-the-Art Urdu Large Language Model for 230M Speakers with Systematic Continued Pre-training},
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author={Hassan, Muhammad Taimoor and Ahmed, Jawad and Awais, Muhammad},
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journal={arXiv preprint arXiv:2601.08141},
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year={2026},
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eprint={2601.08141},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={[https://arxiv.org/abs/2601.08141](https://arxiv.org/abs/2601.08141)},
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doi={10.48550/arXiv.2601.08141}
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}
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```
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config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"dtype": "bfloat16",
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"eos_token_id": 128001,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.3",
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"unsloth_fixed": true,
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"unsloth_version": "2026.1.2",
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"use_cache": true,
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"vocab_size": 128256
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}
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generation_config.json
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": 128001,
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"max_length": 131072,
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"pad_token_id": 128004,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.57.3"
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}
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
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||||
{
|
||||
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:384a7e7c676f7be2e5d2e8449c508be9b00e5b18c5b3c39ebc626e96b3f4b988
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size 17210019
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2068
tokenizer_config.json
Normal file
2068
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user