transformers gguf text-generation-inference long-CoT safetensors 3B Instruct QwQ Qwen2.5 llama-cpp gguf-my-repo text-generation en dataset:amphora/QwQ-LongCoT-130K base_model:prithivMLmods/QwQ-LCoT-3B-Instruct base_model:quantized:prithivMLmods/QwQ-LCoT-3B-Instruct license:creativeml-openrail-m endpoints_compatible region:us conversational
---
base_model: prithivMLmods/QwQ-LCoT-3B-Instruct
datasets:
- amphora/QwQ-LongCoT-130K
language:
- en
library_name: transformers
license: creativeml-openrail-m
pipeline_tag: text-generation
tags:
- text-generation-inference
- long-CoT
- safetensors
- 3B
- Instruct
- QwQ
- Qwen2.5
- llama-cpp
- gguf-my-repo
---

# Triangle104/QwQ-LCoT-3B-Instruct-Q4_K_S-GGUF
This model was converted to GGUF format from [`prithivMLmods/QwQ-LCoT-3B-Instruct`](https://huggingface.co/prithivMLmods/QwQ-LCoT-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/QwQ-LCoT-3B-Instruct) for more details on the model.

---
Model details:
-
The QwQ-LCoT-3B-Instruct model is a lightweight, instruction-tuned language model designed for complex reasoning and explanation tasks. It is fine-tuned on the Qwen2.5-3B-Instruct base model using the QwQ-LongCoT-130K dataset, focusing on long-chain-of-thought (LCoT) reasoning for enhanced logical comprehension and detailed output generation.

 Key Features:

    Long Chain-of-Thought Reasoning:
        Specifically designed to generate comprehensive, step-by-step explanations for complex queries.

    Lightweight and Efficient:
        With only 3 billion parameters, it is optimized for systems with limited computational resources without compromising reasoning capabilities.

    Instruction Optimization:
        Fine-tuned to follow prompts and provide concise, actionable, and structured responses.

Training Details:

    Base Model: Qwen2.5-3B-Instruct
    Dataset: amphora/QwQ-LongCoT-130K
        Comprising 133,000 annotated samples focusing on logical tasks and structured thinking.

Capabilities:

    Text Generation:
        Provides detailed, structured, and logical text outputs tailored to user prompts.

    Reasoning Tasks:
        Solves step-by-step problems in math, logic, and science.

    Educational Assistance:
        Generates coherent explanations for academic and research purposes.

    Dialogue and Summarization:
        Handles conversational queries and summarizes long documents effectively.

Usage Instructions:

    Setup: Download all model files and ensure compatibility with the Hugging Face Transformers library.

    Loading the Model:

    from transformers import AutoModelForCausalLM, AutoTokenizer

    model_name = "prithivMLmods/QwQ-LCoT-3B-Instruct"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)

Generate Long-Chain Reasoning Outputs:

input_text = "Explain the process of photosynthesis step-by-step."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=300, temperature=0.5)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Customize Output Generation:
Modify the generation_config.json file for different scenarios:

    temperature: Controls randomness (lower = deterministic, higher = creative).
    max_length: Sets response length.
    top_p: Adjusts sampling for diversity in outputs.

---
## 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/QwQ-LCoT-3B-Instruct-Q4_K_S-GGUF --hf-file qwq-lcot-3b-instruct-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/QwQ-LCoT-3B-Instruct-Q4_K_S-GGUF --hf-file qwq-lcot-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/QwQ-LCoT-3B-Instruct-Q4_K_S-GGUF --hf-file qwq-lcot-3b-instruct-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/QwQ-LCoT-3B-Instruct-Q4_K_S-GGUF --hf-file qwq-lcot-3b-instruct-q4_k_s.gguf -c 2048
```
