Image-Text-to-Text
MLX
Safetensors
English
Chinese
qwen3_5_moe
omlx
apple-silicon
oq
qwen35moe
Mixture of Experts
mtp
speculative-decoding
imatrix
unsloth-dynamic
agentic-coding
abliterated
uncensored
vision
4-bit precision
conversational
Instructions to use peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP") config = load_config("peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent
How to use peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "peculiar-ragdoll/Cyber-Tiel-Coder-35B-A3B-MLX-oQ4e-MTP" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
card: abliteration warning moved above the fold, strengthened, and made identical across all six repos
Browse files
README.md
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<img src="assets/cybertiel_banner_crt.gif" alt="CyberTiel — TielCoder 35B-A3B, abliterated and cyber-tuned" width="100%">
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</div>
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# All power to all people
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*CyberTiel* outcodes every other 35B-A3B at Q4 quantization (and spring-of-2026 frontier models), while engaging with offensive security work without hesitation or refusal. As a sweet spot between speed and ability, CyberTiel delivers agentic coding solves about 3-4x faster than 3.8-27B dense. This is the first time the frontier coder in this size/speed class is an uncensored model. If you need a safer censored alternative, go for *[TielCoder](https://huggingface.co/peculiar-ragdoll/Tiel-Coder-35B-A3B-MLX-oQ4e)*.
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## Abliterated: Willing, able and slightly unstable
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> ⚠️ WARNING: Abliterated models like CyberTiel are able to say and do things other models refuse, including potentially harmful behaviours. By using it, you agree to take personal responsibility for its behaviour, and show caution. It is entirely up to you — the operator — to ensure your use of CyberTiel is legitimate and harmless, and that the model is safely sandboxed and monitored when running. Much like a knife, abliterated models like CyberTiel can be classified and used as either a tool or a weapon, depending on the context and use case. We carry forward [huihui's original usage warnings](https://huggingface.co/huihui-ai/Huihui-Ornith-1.5-35B-A3B-abliterated#usage-warnings).
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**Abliteration**, also known as "uncensoring" or "ablation", is the suppression of refusal in LLMs. This model has undergone abliteration.
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Unabliterated models sometimes wrongly refuse benign (harmless) requests. With CyberTiel you don't need careful wording to get your work done, and deliberation of refusal does not distract the model's attention or waste tokens, thus increasing its ability to perform legitimate work cleanly. This usually comes at the cost of some small corruption of the original model, which in the case of *CyberTiel* is more than balanced out by the advantages combined with the optimized imatrix and quant strategy, leading to a decisive gain on both SWE-bench-Live (agentic coding) and Cybench (offensive security ability).
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<img src="assets/cybertiel_banner_crt.gif" alt="CyberTiel — TielCoder 35B-A3B, abliterated and cyber-tuned" width="100%">
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</div>
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> ⚠️ **WARNING - Read before use**: Abliterated models like CyberTiel are able to say and do things other models refuse, including potentially harmful behaviours. By using CyberTiel, you agree to take full personal responsibility and liability for your use of it, its behaviour and generated content, and to show caution: it is entirely up to you as the user to ensure your use of CyberTiel is legitimate, legal and harmless, and that the model is safely sandboxed and monitored when running. Much like a knife, abliterated models like CyberTiel can be classified and used as either a tool or a weapon, depending on the context and use case. We carry forward [huihui's original usage warnings](https://huggingface.co/huihui-ai/Huihui-Ornith-1.5-35B-A3B-abliterated#usage-warnings).
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# All power to all people
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*CyberTiel* outcodes every other 35B-A3B at Q4 quantization (and spring-of-2026 frontier models), while engaging with offensive security work without hesitation or refusal. As a sweet spot between speed and ability, CyberTiel delivers agentic coding solves about 3-4x faster than 3.8-27B dense. This is the first time the frontier coder in this size/speed class is an uncensored model. If you need a safer censored alternative, go for *[TielCoder](https://huggingface.co/peculiar-ragdoll/Tiel-Coder-35B-A3B-MLX-oQ4e)*.
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## Abliterated: Willing, able and slightly unstable
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**Abliteration**, also known as "uncensoring" or "ablation", is the suppression of refusal in LLMs. This model has undergone abliteration.
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Unabliterated models sometimes wrongly refuse benign (harmless) requests. With CyberTiel you don't need careful wording to get your work done, and deliberation of refusal does not distract the model's attention or waste tokens, thus increasing its ability to perform legitimate work cleanly. This usually comes at the cost of some small corruption of the original model, which in the case of *CyberTiel* is more than balanced out by the advantages combined with the optimized imatrix and quant strategy, leading to a decisive gain on both SWE-bench-Live (agentic coding) and Cybench (offensive security ability).
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