Triangle104/PocketThinker-QwQ-3B-Instruct-Q4_K_S-GGUF

This model was converted to GGUF format from prithivMLmods/PocketThinker-QwQ-3B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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PocketThinker-QwQ-3B-Instruct

PocketThinker-QwQ-3B-Instruct is based on the Qwen2.5-3B-Instruct architecture, designed as a lightweight and efficient reasoning assistant. It serves as the pocket-sized version of QwQ-LCoT-7B-Instruct, optimized for fast inference while maintaining strong problem-solving and computational capabilities. This model is fine-tuned for enhanced structured reasoning, minimal token wastage, and high-quality technical responses.

Key Improvements

Optimized for Coding: Specializes in generating structured, efficient code with minimal redundancy for smooth execution.

Compact yet Powerful: Maintains strong problem-solving capabilities within a smaller 3B parameter architecture, ensuring accessibility on resource-limited devices.

Advanced Reasoning Capabilities: Excels in algorithmic problem-solving, mathematical reasoning, and structured technical explanations.

Efficient Memory Utilization: Reduces computational overhead while maintaining high-quality outputs.

Focused Output Generation: Avoids unnecessary token generation, ensuring concise and relevant responses.

Intended Use

Code Generation & Optimization: Supports developers in writing, refining, and optimizing code across multiple programming languages.

Algorithm & Mathematical Problem Solving: Delivers precise solutions and structured explanations for complex problems.

Technical Documentation & Explanation: Assists in generating well-structured documentation for libraries, APIs, and coding concepts.

Debugging Assistance: Helps identify and correct errors in code snippets.

Educational Support: Simplifies programming topics for students and learners with clear explanations.

Structured Data Processing: Generates structured outputs like JSON, XML, and tables for data science applications.

Limitations

Hardware Constraints: Although lighter than larger models, still requires a moderately powerful GPU or TPU for optimal performance.

Potential Bias in Responses: Outputs may reflect biases present in training data.

Limited Creativity: May generate variable results in non-technical, creative tasks.

No Real-Time Awareness: Lacks access to real-world events beyond its training cutoff.

Error Propagation in Long Responses: Minor mistakes in early outputs may affect overall coherence in lengthy responses.

Prompt Sensitivity: The effectiveness of responses depends on well-structured prompts.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/PocketThinker-QwQ-3B-Instruct-Q4_K_S-GGUF --hf-file pocketthinker-qwq-3b-instruct-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/PocketThinker-QwQ-3B-Instruct-Q4_K_S-GGUF --hf-file pocketthinker-qwq-3b-instruct-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/PocketThinker-QwQ-3B-Instruct-Q4_K_S-GGUF --hf-file pocketthinker-qwq-3b-instruct-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/PocketThinker-QwQ-3B-Instruct-Q4_K_S-GGUF --hf-file pocketthinker-qwq-3b-instruct-q4_k_s.gguf -c 2048