From 92f8f6e2c0183cad2074a3539874904842da36e4 Mon Sep 17 00:00:00 2001
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Model: prithivMLmods/GWQ2b
Source: Original Platform
---
 .gitattributes                   |   36 +
 README.md                        |  121 ++
 config.json                      |   37 +
 configuration.json               |    1 +
 generation_config.json           |   12 +
 model-00001-of-00002.safetensors |    3 +
 model-00002-of-00002.safetensors |    3 +
 model.safetensors.index.json     |  295 +++++
 special_tokens_map.json          |   34 +
 tokenizer.json                   |    3 +
 tokenizer.model                  |    3 +
 tokenizer_config.json            | 2015 ++++++++++++++++++++++++++++++
 12 files changed, 2563 insertions(+)
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 create mode 100644 README.md
 create mode 100644 config.json
 create mode 100644 configuration.json
 create mode 100644 generation_config.json
 create mode 100644 model-00001-of-00002.safetensors
 create mode 100644 model-00002-of-00002.safetensors
 create mode 100644 model.safetensors.index.json
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 create mode 100644 tokenizer.json
 create mode 100644 tokenizer.model
 create mode 100644 tokenizer_config.json

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diff --git a/README.md b/README.md
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+++ b/README.md
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+---
+license: gemma
+language:
+- en
+base_model:
+- google/gemma-2-2b-it
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- gemma
+- 2b
+- CoT
+- text-generation-inference
+- gwq2b
+- gemma-with-question
+- safetensors
+---
+![gwq2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Ayc6YKE6FKYKb8Mible4z.png)
+<a target="_blank" href="https://huggingface.co/spaces/prithivMLmods/GWQ-2B">
+  <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="gwq2b.hf.space"/>
+</a>
+# **GWQ2b - Gemma with Questions2b**
+
+GWQ2b is a family of lightweight, state-of-the-art open models from Google, built using the same research and technology employed to create the Gemini models. These models are text-to-text, decoder-only large language models, available in English, with open weights for both pre-trained and instruction-tuned variants. GWQ2b models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. GWQ2b is fine-tuned on the Chain of Continuous Thought Synthetic Dataset, built upon the Gemma2forCasualLM architecture.
+
+# **Running GWQ2b Demo**
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ2b")
+model = AutoModelForCausalLM.from_pretrained(
+    "prithivMLmods/GWQ2b",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=32)
+print(tokenizer.decode(outputs[0]))
+```
+
+You can ensure the correct chat template is applied by using `tokenizer.apply_chat_template` as follows:
+```python
+messages = [
+    {"role": "user", "content": "Write me a poem about Machine Learning."},
+]
+input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=256)
+print(tokenizer.decode(outputs[0]))
+```
+# **Key Architecture**
+
+1. **Transformer-Based Design**:  
+   GWQ2b leverages the transformer architecture, utilizing self-attention mechanisms to process input text and capture contextual relationships effectively.
+
+2. **Lightweight and Efficient**:  
+   It is designed to be computationally efficient, with fewer parameters compared to larger models, making it ideal for deployment on resource-constrained devices or environments.
+
+3. **Modular Layers**:  
+   The architecture consists of modular encoder and decoder layers, allowing flexibility in adapting the model for specific tasks like text generation, summarization, or classification.
+
+4. **Attention Mechanisms**:  
+   GWQ2b employs multi-head self-attention to focus on relevant parts of the input text, improving its ability to handle long-range dependencies and complex language structures.
+
+5. **Pre-training and Fine-Tuning**:  
+   The model is pre-trained on large text corpora and can be fine-tuned for specific tasks, such as markdown processing in ReadM.Md, to enhance its performance on domain-specific data.
+
+6. **Scalability**:  
+   The architecture supports scaling up or down based on the application's requirements, balancing performance and resource usage.
+
+7. **Open-Source and Customizable**:  
+   Being open-source, GWQ2b allows developers to modify and extend its architecture to suit specific use cases, such as integrating it into tools like ReadM.Md for markdown-related tasks.
+
+# **Intended Use of GWQ2b (Gemma with Questions2b)**
+
+1. **Question Answering:**  
+   The model excels in generating concise and relevant answers to user-provided queries across various domains.
+
+2. **Summarization:**  
+   It can be used to summarize large bodies of text, making it suitable for news aggregation, academic research, and report generation.
+
+3. **Reasoning Tasks:**  
+   GWQ2b is fine-tuned on the Chain of Continuous Thought Synthetic Dataset, which enhances its ability to perform reasoning, multi-step problem solving, and logical inferences.
+
+4. **Text Generation:**  
+   The model is ideal for creative writing tasks such as generating poems, stories, and essays. It can also be used for generating code comments, documentation, and markdown files.
+
+5. **Instruction Following:**  
+   GWQ2b’s instruction-tuned variant is suitable for generating responses based on user instructions, making it useful for virtual assistants, tutoring systems, and automated customer support.
+
+6. **Domain-Specific Applications:**  
+   Thanks to its modular design and open-source nature, the model can be fine-tuned for specific tasks like legal document summarization, medical record analysis, or financial report generation.
+
+# **Limitations of GWQ2b**
+
+1. **Resource Requirements:**  
+   Although lightweight compared to larger models, the 9B parameter size still requires significant computational resources, including GPUs with large memory for inference.
+
+2. **Knowledge Cutoff:**  
+   The model’s pre-training data may not include recent information, making it less effective for answering queries on current events or newly developed topics.
+
+3. **Bias in Outputs:**  
+   Since the model is trained on publicly available datasets, it may inherit biases present in those datasets, leading to potentially biased or harmful outputs in sensitive contexts.
+
+4. **Hallucinations:**  
+   Like other large language models, GWQ2b can occasionally generate incorrect or nonsensical information, especially when asked for facts or reasoning outside its training scope.
+
+5. **Lack of Common-Sense Reasoning:**  
+   While GWQ2b is fine-tuned for reasoning, it may still struggle with tasks requiring deep common-sense knowledge or nuanced understanding of human behavior and emotions.
+
+6. **Dependency on Fine-Tuning:**  
+   For optimal performance on domain-specific tasks, fine-tuning on relevant datasets is required, which demands additional computational resources and expertise.
+  
+7. **Context Length Limitation:**  
+   The model’s ability to process long documents is limited by its maximum context window size. If the input exceeds this limit, truncation may lead to loss of important information.
\ No newline at end of file
diff --git a/config.json b/config.json
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+++ b/config.json
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+{
+  "_name_or_path": "google/gemma-2-2b-it",
+  "architectures": [
+    "Gemma2ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": 50.0,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "eos_token_id": [
+    1,
+    107
+  ],
+  "final_logit_softcapping": 30.0,
+  "head_dim": 256,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 2304,
+  "initializer_range": 0.02,
+  "intermediate_size": 9216,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma2",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "torch_dtype": "float16",
+  "transformers_version": "4.47.1",
+  "use_cache": true,
+  "vocab_size": 256000
+}
diff --git a/configuration.json b/configuration.json
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\ No newline at end of file
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+  "pad_token_id": 0,
+  "transformers_version": "4.47.1"
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+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "205": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "206": {
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+      "rstrip": false,
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+    },
+    "207": {
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+      "lstrip": false,
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+      "single_word": false,
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+    },
+    "208": {
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+    },
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+    },
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+      "single_word": false,
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+    },
+    "212": {
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+      "special": false
+    },
+    "213": {
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+      "single_word": false,
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+    },
+    "214": {
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+      "single_word": false,
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+    },
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+      "rstrip": false,
+      "single_word": false,
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+    },
+    "216": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255968": {
+      "content": "[toxicity=0]",
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+      "normalized": false,
+      "rstrip": false,
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+      "special": false
+    },
+    "255969": {
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+      "rstrip": false,
+      "single_word": false,
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+    },
+    "255970": {
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+      "lstrip": false,
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+      "rstrip": false,
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+    },
+    "255971": {
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+    },
+    "255972": {
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+    },
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+    },
+    "255977": {
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+      "lstrip": false,
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+    },
+    "255978": {
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+    "255979": {
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+    "255980": {
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+    "255982": {
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+    },
+    "255983": {
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+      "normalized": false,
+      "rstrip": false,
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+    },
+    "255984": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255985": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
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+    },
+    "255986": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
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+    },
+    "255987": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
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+    },
+    "255988": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
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+    },
+    "255989": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+      "special": false
+    },
+    "255990": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255991": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "255992": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "255993": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255994": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255995": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255996": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255997": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255998": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255999": {
+      "content": "<unused99>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<start_of_turn>",
+    "<end_of_turn>"
+  ],
+  "bos_token": "<bos>",
+  "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<eos>",
+  "extra_special_tokens": {},
+  "model_max_length": 8192,
+  "pad_token": "<pad>",
+  "padding_side": "left",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}