README.md

---
base_model: prithivMLmods/PocketThinker-QwQ-3B-Instruct
datasets:
- amphora/QwQ-LongCoT-130K
- amphora/QwQ-LongCoT-130K-2
- amphora/verfiable-25k
- amphora/m-math500
language:
- en
- zh
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- Math
- Code
- Thinker
- Reasoning
- 3B
- QwQ
- Mini
- text-generation-inference
- SFT
- llama-cpp
- gguf-my-repo
---

# Triangle104/PocketThinker-QwQ-3B-Instruct-Q4_K_S-GGUF
This model was converted to GGUF format from [`prithivMLmods/PocketThinker-QwQ-3B-Instruct`](https://huggingface.co/prithivMLmods/PocketThinker-QwQ-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/prithivMLmods/PocketThinker-QwQ-3B-Instruct) for more details on the model.

---
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.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
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:
```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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
```