metadata

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 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card 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 method.

For llama-server from llama.cpp, you can use

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)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

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:

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 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"

./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