metadata

license: creativeml-openrail-m
datasets:
  - amphora/QwQ-LongCoT-130K
language:
  - en
base_model: prithivMLmods/QwQ-LCoT-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
  - Long-CoT
  - Qwen2.5
  - 7B
  - safetensors
  - text-generation-inference
  - QwQ
  - SFT
  - Math
  - Qwen with Questions
  - llama-cpp
  - gguf-my-repo

Triangle104/QwQ-LCoT-7B-Instruct-Q5_K_S-GGUF

This model was converted to GGUF format from prithivMLmods/QwQ-LCoT-7B-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.

Model details:

The QwQ-LCoT-7B-Instruct is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the Qwen2.5-7B base model and has been fine-tuned on the amphora/QwQ-LongCoT-130K dataset, focusing on chain-of-thought (CoT) reasoning.

    Key Features:

Model Size:

7.62B parameters (FP16 precision).

Model Sharding:

The model weights are split into 4 shards (safetensors) for efficient storage and download: model-00001-of-00004.safetensors (4.88 GB) model-00002-of-00004.safetensors (4.93 GB) model-00003-of-00004.safetensors (4.33 GB) model-00004-of-00004.safetensors (1.09 GB)

Tokenizer:

Byte-pair encoding (BPE) based. Files included: vocab.json (2.78 MB) merges.txt (1.82 MB) tokenizer.json (11.4 MB)

Special tokens mapped in special_tokens_map.json (e.g., , ).

Configuration Files:

config.json: Defines model architecture and hyperparameters. generation_config.json: Settings for inference and text generation tasks.

    Training Dataset:

Dataset Name: amphora/QwQ-LongCoT-130K
Size: 133k examples.
Focus: Chain-of-Thought reasoning for complex tasks.

    Use Cases:

Instruction Following: Handle user instructions effectively, even for multi-step tasks.

Reasoning Tasks:

Perform logical reasoning and generate detailed step-by-step solutions.

Text Generation:

Generate coherent, context-aware responses.

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/QwQ-LCoT-7B-Instruct-Q5_K_S-GGUF --hf-file qwq-lcot-7b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/QwQ-LCoT-7B-Instruct-Q5_K_S-GGUF --hf-file qwq-lcot-7b-instruct-q5_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/QwQ-LCoT-7B-Instruct-Q5_K_S-GGUF --hf-file qwq-lcot-7b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/QwQ-LCoT-7B-Instruct-Q5_K_S-GGUF --hf-file qwq-lcot-7b-instruct-q5_k_s.gguf -c 2048