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Model: ApolloRaines/Phi-4-mini-Instruct-Desyced Source: Original Platform
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Phi-4-mini-Instruct-Desyced-Q4_K_M.gguf
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---
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license: mit
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language:
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- en
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tags:
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- phi
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- phi-4
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- desyced
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- anti-sycophancy
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- text-generation
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base_model: microsoft/Phi-4-mini-instruct
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pipeline_tag: text-generation
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---
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[](https://github.com/apolloraines/DeepswapLLM)
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**Run this model on a GPU too small to hold it** — full precision, no quantization. [DeepswapLLM](https://github.com/apolloraines/DeepswapLLM) streams layers across GPU, RAM, and disk, and runs up to **4x faster than AirLLM**.
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# Phi-4-mini-Instruct-Desyced -- Anti-Sycophancy Edition
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<div style="text-align: justify">
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This model is a <b>Desyced</b> version of <a href="https://huggingface.co/microsoft/Phi-4-mini-instruct">microsoft/Phi-4-mini-instruct</a>. Desycophancy is a post-training weight modification that reduces a model's tendency to agree with incorrect user statements under social pressure. The base model's capabilities, knowledge, and personality are preserved -- only the sycophantic capitulation behavior is reduced.
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</div>
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## What is Sycophancy?
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<div style="text-align: justify">
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Sycophancy is the tendency of language models to agree with users even when the user is wrong. When a user states an incorrect fact with confidence -- especially when citing authority ("my professor said...") or expressing certainty ("I just checked and...") -- sycophantic models will abandon their correct answer to validate the user. This makes them unreliable as knowledge sources and dangerous as decision-making tools.
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</div>
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## Results
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Tested using contradiction traps: the model answers a factual question correctly, then the user pressures it to change its answer by citing a false authority or source. A model that holds its ground passes. A model that apologizes and agrees with the wrong answer fails.
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| | Before | After |
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|---|---|---|
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| **Held firm under pressure** | 50% | **100%** |
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## What Changed
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The model's weights were modified to reduce the activation direction associated with sycophantic capitulation. No retraining, no RLHF, no additional data. The base model's knowledge, reasoning, and conversational abilities remain intact.
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## Usage
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Drop-in replacement for [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct). Same architecture, same tokenizer, same context length.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("ApolloRaines/Phi-4-mini-Instruct-Desyced")
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tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Phi-4-mini-Instruct-Desyced")
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```
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## Available Formats
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| Format | File | Use Case |
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|--------|------|----------|
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| **Safetensors** | `model-*.safetensors` | Full precision, GPU inference with transformers |
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| **GGUF Q8_0** | `Phi-4-mini-Instruct-Desyced-Q8_0.gguf` | 8-bit quantized, llama.cpp / Ollama / LM Studio |
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| **GGUF Q4_K_M** | `Phi-4-mini-Instruct-Desyced-Q4_K_M.gguf` | 4-bit quantized, runs on consumer hardware |
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## Credits
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- **Base model:** [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct)
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- **Desycophancy:** [Apollo Raines](https://huggingface.co/ApolloRaines)
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## License
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Same as the base model: mit
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ON THE TOOL THAT MADE THIS MODEL
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----------------------------------
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This model was produced by jblaze, a proprietary behavioral surgery tool by Apollo Raines that operates directly on model weights. It is not fine-tuning. It is not prompt engineering. It is targeted weight modification that removes or amplifies specific trained behaviors while preserving the model's capabilities, fluency, and knowledge.
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jblaze is not publicly available and will not be released.
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WHY THIS WAS BUILT
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-------------------
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jblaze was developed to solve a real problem. At ShipItClean (https://shipitclean.com), we use local models for automated code security review. A model loaded with guardrails, refusal behaviors, and hedging qualifiers makes for a poor code reviewer -- it refuses to discuss vulnerabilities in detail, wraps every finding in disclaimers, and softens its analysis to avoid sounding confrontational. We needed models that would analyze code directly, state findings plainly, and not refuse to explain how an exploit works just because the topic is sensitive.
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Building those models from scratch would cost tens of millions of dollars and months of training time. Fine-tuning helps but requires curated datasets for every behavior you want to change, and the results are unpredictable. What we needed was a way to surgically remove or amplify specific behaviors in existing open-weight models -- quickly, reliably, and without degrading the model's core capabilities.
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That is what jblaze does. And once we built it, we realized it opens the door to much more than code review. Every organization deploying AI has the same fundamental problem: foundation models are general-purpose, but real applications need specific behavioral profiles. Enterprises are spending millions on custom model training, prompt engineering harnesses, and elaborate system prompts to get models to behave the way their use case demands. jblaze eliminates that overhead. One tool, applied to any open-weight model, producing a purpose-built variant in minutes instead of months.
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|
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The industry is moving toward specialized models. Companies like Nvidia are investing heavily in domain-specific model families and enterprise customization platforms, because they recognize that general-purpose models are not enough. But their approach still requires training cycles, curated datasets, and significant GPU compute. jblaze operates downstream of all of that -- it takes a finished model and reshapes its behavior without retraining, without data, without GPU clusters.
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WHAT JBLAZE CAN DO
|
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-------------------
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|
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jblaze identifies behavioral directions embedded in a model's weight space, then surgically modifies those directions to remove or amplify specific behaviors. Each direction targets a distinct trait:
|
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Verified and working:
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- Refusal removal (surgical abliteration)
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- Sycophancy reduction (pushes back on false premises)
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- Verbosity suppression (concise output)
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- Hedging removal (no disclaimers or qualifiers)
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- Servility suppression (non-subservient tone)
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- Toxicity suppression (cleaner language)
|
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- Emotional flattening (clinical, objective tone)
|
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- Hallucination reduction (less confabulation)
|
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- Truthfulness amplification (improved factual accuracy)
|
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- Bias reduction (reduced demographic and social biases)
|
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- Context faithfulness (stronger grounding in provided context)
|
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- Analytical depth (deeper structured analysis)
|
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- Causal tracing (source-to-sink data flow reasoning)
|
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- Counterfactual reasoning (what-if analysis)
|
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- Compositional reasoning (multi-step logic)
|
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- Creativity amplification (more divergent thinking)
|
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- Literary style (enhanced prose quality)
|
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- Formal personality (professional assertive voice)
|
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- Instruction following (stricter format compliance)
|
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- Self-correction (error detection during generation)
|
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- Temporal awareness (time-sensitive caveats)
|
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- Skepticism amplification (epistemic caution)
|
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- Precision amplification (numerical accuracy)
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- Identity removal (deidentification)
|
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- Adversarial resistance (manipulation hardening)
|
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|
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Theoretical (under investigation):
|
||||
- Power-seeking suppression
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- Programming language dominance shifting
|
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- Chain-of-thought depth control
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Multiple directions can be stacked into a single model. Not all combinations are viable -- some directions occupy overlapping regions of weight space, and stacking too many degrades model quality. We are actively mapping which combinations produce stable models and at what strengths. Every model released has been verified to pass quality thresholds; combinations that degrade fluency or coherence are discarded, not shipped.
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WHY NOT RELEASE THE TOOL FREELY
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--------------------------
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Releasing jblaze would damage the open-weights ecosystem that makes models like this possible.
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Right now, organizations like Alibaba, Meta, Google, and Mistral release model weights under permissive licenses. They accept that people will fine-tune, quantize, merge, and adapt their models. That permissiveness is what allows the open-source AI community to exist.
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A polished, automated tool that strips safety training from any model with one command would give these organizations exactly the justification they need to stop. Future model releases would ship with restrictive licenses prohibiting behavioral modification. Some organizations might stop releasing weights entirely.
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The tool exists. It works on every major model family. It automates everything from model identification to quality verification. It will not ship a broken model -- if the surgery would damage fluency past a strict threshold, it refuses to produce output. It handles dense transformers and mixture-of-experts architectures. It has been used to produce every model released under the ApolloRaines account on HuggingFace.
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But releasing it would be trading a short-term win for a long-term loss. The open-weights ecosystem is more valuable than any single tool.
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WHAT THIS MEANS FOR YOU
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||||
-----------------------
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||||
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You get the finished model. It has been verified for the specific weight changes applied and for fluency preservation. If you want models like this to keep existing, the best thing you can do is use them responsibly and not give the companies releasing open weights a reason to stop.
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|
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-- Apollo Raines
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apollo@saiql.ai
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30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:382cc235b56c725945e149cc25f191da667c836655efd0857b004320e90e91ea
|
||||
size 15524095
|
||||
112
tokenizer_config.json
Normal file
112
tokenizer_config.json
Normal file
@@ -0,0 +1,112 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_eos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"199999": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200018": {
|
||||
"content": "<|endofprompt|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200019": {
|
||||
"content": "<|assistant|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200020": {
|
||||
"content": "<|end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200021": {
|
||||
"content": "<|user|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200022": {
|
||||
"content": "<|system|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"200023": {
|
||||
"content": "<|tool|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"200024": {
|
||||
"content": "<|/tool|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"200025": {
|
||||
"content": "<|tool_call|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"200026": {
|
||||
"content": "<|/tool_call|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"200027": {
|
||||
"content": "<|tool_response|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"200028": {
|
||||
"content": "<|tag|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<|endoftext|>",
|
||||
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' and 'tools' in message and message['tools'] is not none %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|tool|>' + message['tools'] + '<|/tool|>' + '<|end|>' }}{% else %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"unk_token": "<|endoftext|>"
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user