Triangle104/QwQ-LCoT2-7B-Instruct-Q4_K_S-GGUF

This model was converted to GGUF format from prithivMLmods/QwQ-LCoT2-7B-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-LCoT2-7B-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 chain of thought reasoning datasets, focusing on chain-of-thought (CoT) reasoning for problems. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.

    Quickstart with Transformers

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/QwQ-LCoT2-7B-Instruct"

model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "How many r in strawberry." messages = [ {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

    Intended Use

The QwQ-LCoT2-7B-Instruct model is designed for advanced reasoning and instruction-following tasks, with specific applications including:

Instruction Following: Providing detailed and step-by-step guidance for a wide range of user queries. Logical Reasoning: Solving problems requiring multi-step thought processes, such as math problems or complex logic-based scenarios. Text Generation: Crafting coherent, contextually relevant, and well-structured text in response to prompts. Problem-Solving: Analyzing and addressing tasks that require chain-of-thought (CoT) reasoning, making it ideal for education, tutoring, and technical support. Knowledge Enhancement: Leveraging reasoning datasets to offer deeper insights and explanations for a wide variety of topics.

    Limitations

Data Bias: As the model is fine-tuned on specific datasets, its outputs may reflect inherent biases from the training data. Context Limitation: Performance may degrade for tasks requiring knowledge or reasoning that significantly exceeds the model's pretraining or fine-tuning context. Complexity Ceiling: While optimized for multi-step reasoning, exceedingly complex or abstract problems may result in incomplete or incorrect outputs. Dependency on Prompt Quality: The quality and specificity of the user prompt heavily influence the model's responses. Non-Factual Outputs: Despite being fine-tuned for reasoning, the model can still generate hallucinated or factually inaccurate content, particularly for niche or unverified topics. Computational Requirements: Running the model effectively requires significant computational resources, particularly when generating long sequences or handling high-concurrency workloads.

---

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-LCoT2-7B-Instruct-Q4_K_S-GGUF --hf-file qwq-lcot2-7b-instruct-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/QwQ-LCoT2-7B-Instruct-Q4_K_S-GGUF --hf-file qwq-lcot2-7b-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/QwQ-LCoT2-7B-Instruct-Q4_K_S-GGUF --hf-file qwq-lcot2-7b-instruct-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/QwQ-LCoT2-7B-Instruct-Q4_K_S-GGUF --hf-file qwq-lcot2-7b-instruct-q4_k_s.gguf -c 2048