---
license: apache-2.0
language:
- en
base_model: prithivMLmods/QwQ-LCoT2-7B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- LCoT
- Qwen
- v2
- llama-cpp
- gguf-my-repo
datasets:
- PowerInfer/QWQ-LONGCOT-500K
- AI-MO/NuminaMath-CoT
- prithivMLmods/Math-Solve
- amphora/QwQ-LongCoT-130K
- prithivMLmods/Deepthink-Reasoning
model-index:
- name: QwQ-LCoT2-7B-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: IFEval (0-Shot)
      type: wis-k/instruction-following-eval
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: inst_level_strict_acc and prompt_level_strict_acc
      value: 55.76
      name: averaged accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: BBH (3-Shot)
      type: SaylorTwift/bbh
      split: test
      args:
        num_few_shot: 3
    metrics:
    - type: acc_norm
      value: 34.37
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MATH Lvl 5 (4-Shot)
      type: lighteval/MATH-Hard
      split: test
      args:
        num_few_shot: 4
    metrics:
    - type: exact_match
      value: 22.21
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GPQA (0-shot)
      type: Idavidrein/gpqa
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 6.38
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MuSR (0-shot)
      type: TAUR-Lab/MuSR
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      value: 15.75
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU-PRO (5-shot)
      type: TIGER-Lab/MMLU-Pro
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 37.13
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
      name: Open LLM Leaderboard
---

# Triangle104/QwQ-LCoT2-7B-Instruct-Q5_K_M-GGUF
This model was converted to GGUF format from [`prithivMLmods/QwQ-LCoT2-7B-Instruct`](https://huggingface.co/prithivMLmods/QwQ-LCoT2-7B-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-LCoT2-7B-Instruct) 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)

```bash
brew install llama.cpp

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

### CLI:
```bash
llama-cli --hf-repo Triangle104/QwQ-LCoT2-7B-Instruct-Q5_K_M-GGUF --hf-file qwq-lcot2-7b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/QwQ-LCoT2-7B-Instruct-Q5_K_M-GGUF --hf-file qwq-lcot2-7b-instruct-q5_k_m.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-LCoT2-7B-Instruct-Q5_K_M-GGUF --hf-file qwq-lcot2-7b-instruct-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/QwQ-LCoT2-7B-Instruct-Q5_K_M-GGUF --hf-file qwq-lcot2-7b-instruct-q5_k_m.gguf -c 2048
```