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Model: prithivMLmods/Llama-Thinker-3B-Preview2
Source: Original Platform

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README.md

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+---
+license: creativeml-openrail-m
+library_name: transformers
+tags:
+- deep_think
+- reasoning
+- chain_of_thought
+- chain_of_thinking
+- prev_2
+- self_reasoning
+language:
+- en
+base_model:
+- prithivMLmods/Llama-Thinker-3B-Preview
+pipeline_tag: text-generation
+---
+![lp2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/n6K9uTYIG6LK_GHYKx0yX.png)
+
+# **Llama-Thinker-3B-Preview2**
+
+Llama-Thinker-3B-Preview2 is a pretrained and instruction-tuned generative model designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.
+
+Model Architecture: [ Based on Llama 3.2 ] is an autoregressive language model that uses an optimized transformer architecture. The tuned versions undergo supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
+
+# **Use with transformers**
+
+Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
+
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
+
+```python
+import torch
+from transformers import pipeline
+
+model_id = "prithivMLmods/Llama-Thinker-3B-Preview2"
+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])
+```
+
+Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
+
+# **Use with `llama`**
+
+Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
+
+To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
+
+```
+huggingface-cli download prithivMLmods/Llama-Thinker-3B-Preview2 --include "original/*" --local-dir Llama-Thinker-3B-Preview2
+```
+
+Here’s a version tailored for the **Llama-Thinker-3B-Preview2-GGUF** model:
+
+---
+
+# **How to Run Llama-Thinker-3B-Preview2 on Ollama Locally**
+
+This guide demonstrates how to run the **Llama-Thinker-3B-Preview2-GGUF** model locally using Ollama. The model is instruction-tuned for multilingual tasks and complex reasoning, making it highly versatile for a wide range of use cases. By the end, you'll be equipped to run this and other open-source models with ease.
+
+---
+
+## Example 1: How to Run the Llama-Thinker-3B-Preview2 Model
+
+The **Llama-Thinker-3B-Preview2** model is a pretrained and instruction-tuned LLM, designed for complex reasoning tasks across multiple languages. In this guide, we'll interact with it locally using Ollama, with support for quantized models.
+
+### Step 1: Download the Model
+
+First, download the **Llama-Thinker-3B-Preview2-GGUF** model using the following command:
+
+```bash
+ollama run llama-thinker-3b-preview2.gguf
+```
+
+### Step 2: Model Initialization and Download
+
+Once the command is executed, Ollama will initialize and download the necessary model files. You should see output similar to this:
+
+```plaintext
+pulling manifest
+pulling a12cd3456efg... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 3.2 GB
+pulling 9f87ghijklmn... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 6.5 KB
+verifying sha256 digest
+writing manifest
+removing any unused layers
+success
+>>> Send a message (/? for help)
+```
+
+### Step 3: Interact with the Model
+
+Once the model is fully loaded, you can interact with it by sending prompts. For example, let's ask:
+
+```plaintext
+>>> How can you assist me today?
+```
+
+A sample response might look like this [may / maynot be identical]:
+
+```plaintext
+I am Llama-Thinker-3B-Preview2, an advanced AI language model designed to assist with complex reasoning, multilingual tasks, and general-purpose queries. Here are a few things I can help you with:
+
+1. Answering complex questions in multiple languages.
+2. Assisting with creative writing, content generation, and problem-solving.
+3. Providing detailed summaries and explanations.
+4. Translating text across different languages.
+5. Generating ideas for personal or professional use.
+6. Offering insights on technical topics.
+
+Feel free to ask me anything you'd like assistance with!
+```
+
+### Step 4: Exit the Program
+
+To exit the program, simply type:
+
+```plaintext
+/exit
+```
+
+---
+
+## Example 2: Using Multi-Modal Models (Future Use)
+
+In the future, Ollama may support multi-modal models where you can input both text and images for advanced interactions. This section will be updated as new capabilities become available.
+
+---
+
+## Notes on Using Quantized Models
+
+Quantized models like **llama-thinker-3b-preview2.gguf** are optimized for efficient performance on local systems with limited resources. Here are some key points to ensure smooth operation:
+
+1. **VRAM/CPU Requirements**: Ensure your system has adequate VRAM or CPU resources to handle model inference.
+2. **Model Format**: Use the `.gguf` model format for compatibility with Ollama.
+
+---
+
+# **Conclusion**
+
+Running the **Llama-Thinker-3B-Preview2** model locally using Ollama provides a powerful way to leverage open-source LLMs for complex reasoning and multilingual tasks. By following this guide, you can explore other models and expand your use cases as new models become available.
+
+---

config.json

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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": [
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+    128008,
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+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
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+  "num_key_value_heads": 8,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
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+    "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": "4.47.1",
+  "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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+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.47.1"
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special_tokens_map.json

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