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Model: prithivMLmods/Llama3.2-1B-Grpo-Exp
Source: Original Platform

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README.md

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+---
+license: llama3.2
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+---
+![czsdca.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vcCVfEFpN-UQ_ZX7D-tUZ.png)
+
+# **Llama3.2-1B-Grpo-Exp**  
+> The **Llama3.2-1B-Grpo-Exp** is a fine-tuned version of the **Llama-3.1-8B** base model, further enhanced with the **GSM8K dataset** for superior text generation and mathematical reasoning. This model is designed for advanced reasoning, structured problem-solving, and contextually rich outputs, making it an excellent choice for applications in **education, programming, research, and creative writing**.  
+
+With its optimized architecture, **Llama3.2-1B-Grpo-Exp** excels at:  
+- **Logical reasoning** and **step-by-step problem-solving**  
+- **Mathematical and coding tasks**, leveraging specialized expert models  
+- **Generating long-form content** (up to 8K tokens) with improved coherence  
+- **Understanding structured data**, including tables and JSON outputs  
+- **Following instructions** and **adapting to diverse system prompts**, making it ideal for chatbots and AI assistants  
+
+## **Key Features**  
+- **Supports long-context processing** of up to **128K tokens**   
+- **Fine-tuned using Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF)**  
+
+## **Model Architecture**  
+Llama3.2-1B-Grpo-Exp is built on the optimized transformer architecture of **Llama-3.1-8B**, integrating **enhanced dataset logits from GSM8K** for better mathematical reasoning and structured output generation.  
+
+## **Using with transformers**  
+To run conversational inference using `transformers >= 4.43.0`, use the `pipeline` abstraction or leverage the `generate()` function with the Auto classes.  
+
+Ensure your environment is updated with:  
+```bash  
+pip install --upgrade transformers  
+```  
+
+### **Example Usage**  
+```python  
+import torch  
+from transformers import pipeline  
+
+model_id = "prithivMLmods/Llama3.2-1B-Grpo-Exp"  
+pipe = pipeline(  
+    "text-generation",  
+    model=model_id,  
+    torch_dtype=torch.bfloat16,  
+    device_map="auto",  
+)  
+
+messages = [  
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},  
+    {"role": "user", "content": "Who are you?"},  
+]  
+
+outputs = pipe(  
+    messages,  
+    max_new_tokens=256,  
+)  
+print(outputs[0]["generated_text"][-1])  
+```  
+
+## **Intended Use**  
+**Llama3.2-1B-Grpo-Exp** is designed for a wide range of applications requiring deep reasoning, structured outputs, and logical text generation. It is particularly suited for:  
+
+- **Education & Research**: Generating detailed explanations, step-by-step solutions, and structured academic content.  
+- **Programming & Code Generation**: Assisting in code writing, debugging, and algorithm explanations with improved logic structuring.  
+- **AI Chatbots & Assistants**: Providing context-aware, instruction-following responses for conversational AI applications.  
+- **Creative Writing**: Generating high-quality stories, articles, and structured narratives with coherence.  
+- **Data Analysis & Structured Output Generation**: Interpreting and generating JSON, tables, and formatted outputs for structured data processing.  
+
+## **Limitations**  
+While **Llama3.2-1B-Grpo-Exp** is optimized for deep reasoning and structured outputs, it has some limitations:  
+
+1. **Not a Real-time Knowledge Source**  
+   - The model is trained on a fixed dataset and does not have real-time internet access. It may not provide up-to-date information on rapidly evolving topics.  
+
+2. **Potential Biases**  
+   - As with all AI models, responses may reflect biases present in the training data. Users should critically evaluate outputs, especially in sensitive domains.  
+
+3. **Mathematical & Logical Reasoning Constraints**  
+   - While strong in step-by-step reasoning, it may occasionally produce incorrect mathematical calculations or logical inconsistencies. External verification is recommended for critical applications.  
+
+4. **Handling of Extremely Long Contexts**  
+   - While it supports up to 128K tokens, efficiency and coherence may degrade when processing very long documents or conversations.  
+
+5. **Limited Handling of Ambiguity**  
+   - The model may struggle with highly ambiguous or context-dependent queries, sometimes generating plausible but incorrect responses.  
+
+6. **Ethical & Compliance Considerations**  
+   - Not intended for generating misinformation, automating legal or medical decisions, or other high-risk applications without human oversight.

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.48.3",
+  "use_cache": true,
+  "vocab_size": 128256
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "max_length": 131072,
+  "pad_token_id": 0,
+  "transformers_version": "4.48.3"
+}

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "eos_token": {
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+    "lstrip": false,
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+  "pad_token": {
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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