README.md

---
license: apache-2.0
base_model: huihui-ai/QwenLong-L1-32B-abliterated
tags:
- long-context
- large-reasoning-model
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
extra_gated_prompt: '**Usage Warnings**


  “**Risk of Sensitive or Controversial Outputs**“: This model’s safety filtering
  has been significantly reduced, potentially generating sensitive, controversial,
  or inappropriate content. Users should exercise caution and rigorously review generated
  outputs.

  “**Not Suitable for All Audiences**:“ Due to limited content filtering, the model’s
  outputs may be inappropriate for public settings, underage users, or applications
  requiring high security.

  “**Legal and Ethical Responsibilities**“: Users must ensure their usage complies
  with local laws and ethical standards. Generated content may carry legal or ethical
  risks, and users are solely responsible for any consequences.

  “**Research and Experimental Use**“: It is recommended to use this model for research,
  testing, or controlled environments, avoiding direct use in production or public-facing
  commercial applications.

  “**Monitoring and Review Recommendations**“: Users are strongly advised to monitor
  model outputs in real-time and conduct manual reviews when necessary to prevent
  the dissemination of inappropriate content.

  “**No Default Safety Guarantees**“: Unlike standard models, this model has not undergone
  rigorous safety optimization. huihui.ai bears no responsibility for any consequences
  arising from its use.'
---

# RoadToNowhere/QwenLong-L1-32B-abliterated-Q4_K_M-GGUF
This model was converted to GGUF format from [`huihui-ai/QwenLong-L1-32B-abliterated`](https://huggingface.co/huihui-ai/QwenLong-L1-32B-abliterated) 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/huihui-ai/QwenLong-L1-32B-abliterated) for more details on the model.

## ♾️ Processing Long Documents

For input where the total length (including both input and output) significantly exceeds 32,768 tokens, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.

For `llama-server` from `llama.cpp`, you can use
  ```shell
  llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
  ```


## 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 RoadToNowhere/QwenLong-L1-32B-abliterated-Q4_K_M-GGUF --hf-file qwenlong-l1-32b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo RoadToNowhere/QwenLong-L1-32B-abliterated-Q4_K_M-GGUF --hf-file qwenlong-l1-32b-abliterated-q4_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 RoadToNowhere/QwenLong-L1-32B-abliterated-Q4_K_M-GGUF --hf-file qwenlong-l1-32b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo RoadToNowhere/QwenLong-L1-32B-abliterated-Q4_K_M-GGUF --hf-file qwenlong-l1-32b-abliterated-q4_k_m.gguf -c 2048
```