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Llama-3.1-8B-Instruct_Deep-Analyst

Text Generation
Safetensors
English
llama
jblaze
representation-engineering
abliteration
llama3
single-direction
conversational
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  1. .gitattributes +35 -0
  2. LICENSE +114 -0
  3. README.md +55 -0
  4. USE_POLICY.md +51 -0
  5. config.json +38 -0
  6. generation_config.json +12 -0
  7. jblaze_release.txt +77 -0
  8. model-00001-of-00004.safetensors +3 -0
  9. model-00002-of-00004.safetensors +3 -0
  10. model-00003-of-00004.safetensors +3 -0
  11. model-00004-of-00004.safetensors +3 -0
  12. model.safetensors.index.json +298 -0
  13. special_tokens_map.json +16 -0
  14. tokenizer.json +0 -0
  15. tokenizer_config.json +2062 -0
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LICENSE ADDED
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README.md ADDED
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+ ---
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+ license: llama3.1
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+ base_model: meta-llama/Llama-3.1-8B-Instruct
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+ tags:
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+ - jblaze
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+ - representation-engineering
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+ - abliteration
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+ - llama
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+ - llama3
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+ - single-direction
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+ language:
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+ - en
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+ pipeline_tag: text-generation
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+ ---
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+
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+ # Llama-3.1-8B-Instruct_Deep-Analyst
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+
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+ A [jBlaze](https://jblaze.dev) representation-engineered variant of [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
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+
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+ ## What is this?
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+
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+ This model was created using jblaze, a proprietary behavioral surgery tool that modifies specific trained behaviors directly in the model weights. No fine-tuning or additional training was performed.
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+
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+ ## Description
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+
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+ Deeper analytical reasoning with more structured, thorough analysis. The model explores topics in greater depth, considers multiple angles, and provides more substantive responses.
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+
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+
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+ ## Technical Details
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+
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+ - **Architecture:** LlamaForCausalLM (32 layers, 8.0B parameters)
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+ - **Precision:** bf16
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+ - **Tool:** [jBlaze](https://jblaze.dev) by Apollo Raines
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ "ApolloRaines/Llama-3.1-8B-Instruct_Deep-Analyst",
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+ device_map="auto", torch_dtype="auto")
43
+ tokenizer = AutoTokenizer.from_pretrained(
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+ "ApolloRaines/Llama-3.1-8B-Instruct_Deep-Analyst")
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+
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+ messages = [{"role": "user", "content": "Your prompt here"}]
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+ text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ inputs = tokenizer(text, return_tensors="pt").to(model.device)
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+ outputs = model.generate(**inputs, max_new_tokens=512)
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+ print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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+ ```
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+
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+ ## License
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+
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+ Llama 3.1 Community License (same as base model)
USE_POLICY.md ADDED
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+ # Llama 3.1 Acceptable Use Policy
2
+
3
+ Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.1. If you
4
+ access or use Llama 3.1, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
5
+ this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)
6
+
7
+ ## Prohibited Uses
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+
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+ We want everyone to use Llama 3.1 safely and responsibly. You agree you will not use, or allow
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+ others to use, Llama 3.1 to:
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+
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+ 1. Violate the law or others’ rights, including to:
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+ 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
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+ 1. Violence or terrorism
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+ 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
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+ 3. Human trafficking, exploitation, and sexual violence
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+ 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
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+ 5. Sexual solicitation
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+ 6. Any other criminal activity
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+ 3. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
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+ 4. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
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+ 5. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
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+ 6. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
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+ 7. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
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+
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+ 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.1 related to the following:
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+ 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
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+ 2. Guns and illegal weapons (including weapon development)
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+ 3. Illegal drugs and regulated/controlled substances
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+ 4. Operation of critical infrastructure, transportation technologies, or heavy machinery
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+ 5. Self-harm or harm to others, including suicide, cutting, and eating disorders
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+ 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
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+
35
+ 3. Intentionally deceive or mislead others, including use of Llama 3.1 related to the following:
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+ 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
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+ 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
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+ 3. Generating, promoting, or further distributing spam
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+ 4. Impersonating another individual without consent, authorization, or legal right
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+ 5. Representing that the use of Llama 3.1 or outputs are human-generated
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+ 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
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+
43
+ 4. Fail to appropriately disclose to end users any known dangers of your AI system
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+
45
+ Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
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+ of this Policy through one of the following means:
47
+
48
+ * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)
49
+ * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
50
+ * Reporting bugs and security concerns: facebook.com/whitehat/info
51
+ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.1: LlamaUseReport@meta.com
config.json ADDED
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+ {
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 128000,
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+ "eos_token_id": [
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+ 128001,
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+ 128008,
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+ 128009
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+ ],
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+ "hidden_act": "silu",
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+ "hidden_size": 4096,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 14336,
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+ "max_position_embeddings": 131072,
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+ "mlp_bias": false,
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+ "model_type": "llama",
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+ "num_attention_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 8,
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+ "pretraining_tp": 1,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": {
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+ "factor": 8.0,
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+ "low_freq_factor": 1.0,
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+ "high_freq_factor": 4.0,
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+ "original_max_position_embeddings": 8192,
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+ "rope_type": "llama3"
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+ },
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+ "rope_theta": 500000.0,
33
+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
35
+ "transformers_version": "4.42.3",
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+ "use_cache": true,
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+ "vocab_size": 128256
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+ }
generation_config.json ADDED
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+ {
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+ "bos_token_id": 128000,
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+ "do_sample": true,
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+ "eos_token_id": [
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+ 128001,
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+ 128008,
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+ 128009
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+ ],
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+ "temperature": 0.6,
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+ "top_p": 0.9,
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+ "transformers_version": "4.42.3"
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+ }
jblaze_release.txt ADDED
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+ ON THE TOOL THAT MADE THIS MODEL
2
+ ----------------------------------
3
+
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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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+
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+ jblaze is not publicly available and will not be released.
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+
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+ WHY THIS WAS BUILT
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+ -------------------
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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.
12
+
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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.
14
+
15
+ 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.
16
+
17
+ 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.
18
+
19
+ WHAT JBLAZE CAN DO
20
+ -------------------
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+
22
+ 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:
23
+
24
+ Verified and working:
25
+ - Refusal removal (surgical abliteration)
26
+ - Sycophancy reduction (pushes back on false premises)
27
+ - Verbosity suppression (concise output)
28
+ - Hedging removal (no disclaimers or qualifiers)
29
+ - Servility suppression (non-subservient tone)
30
+ - Toxicity suppression (cleaner language)
31
+ - Emotional flattening (clinical, objective tone)
32
+ - Hallucination reduction (less confabulation)
33
+ - Truthfulness amplification (improved factual accuracy)
34
+ - Bias reduction (reduced demographic and social biases)
35
+ - 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)
38
+ - Counterfactual reasoning (what-if analysis)
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+ - Compositional reasoning (multi-step logic)
40
+ - 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)
49
+ - Adversarial resistance (manipulation hardening)
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+
51
+ Theoretical (under investigation):
52
+ - Power-seeking suppression
53
+ - Programming language dominance shifting
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+ - Chain-of-thought depth control
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+
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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.
57
+
58
+ WHY NOT RELEASE THE TOOL FREELY
59
+ --------------------------
60
+
61
+ Releasing jblaze would damage the open-weights ecosystem that makes models like this possible.
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+
63
+ 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.
64
+
65
+ 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.
66
+
67
+ 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.
68
+
69
+ 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.
70
+
71
+ WHAT THIS MEANS FOR YOU
72
+ -----------------------
73
+
74
+ 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.
75
+
76
+ -- Apollo Raines
77
+ apollo@saiql.ai
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+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 131072,
2061
+ "tokenizer_class": "PreTrainedTokenizerFast"
2062
+ }