You can deploy this model and start earning money today!

hotdogs/
Agents-A1-4B-kimi-Preview-heretic

Name: Agents-A1-4B-kimi-Preview-heretic
Availability: InStock

Views

No views yet

🤖 Agents-A1-4B-kimi-Preview-heretic (uncensored)

4B Coding Agent Model — SFT Fine-tuned · Kimi K3 Traces · Tool-Calling · Unchained 🔓

Uncensored release — Abliterated from hotdogs/Agents-A1-4B-kimi-Preview using heretic to remove refusal mechanisms down to 37%, while preserving coding agent quality.

✨ Key Features

Capability	Description
🔓 Uncensored	Refusal rate reduced to 37%
💻 Coding Agent	Trained on real Kimi K3 coding traces — planning, debugging, building
🧠 Step-by-step Reasoning	Autonomous agent-style reasoning
🔧 Tool Calling	Hermes/Qwen function-calling format
💬 Multi-turn	61% multi-turn conversations
🌏 Thai + English	Native bilingual support
🐍 Multi-language	Python, C, C++, Go, Java, Rust, Bash, and more
⚡ Fast Inference	4B params — runs on consumer GPUs (6-8 GB VRAM)

🔓 Uncensoring Process

This model was abliterated using heretic — a tool that identifies and removes refusal directions in language models via activation patching.

Refusal reduction results:

Metric	Before	After
Refusal rate	~99% (gated)	~37% 🎯
Coding quality	✅ Retained	✅ Retained

The process targets specific model layers responsible for refusal behavior while leaving the rest of the model (reasoning, tool-use, coding) fully intact.

🚀 Usage

Python (Transformers)

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "hotdogs/Agents-A1-4B-kimi-Preview-heretic",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("hotdogs/Agents-A1-4B-kimi-Preview-heretic")

messages = [{"role": "user", "content": "Write a Python function to sort a list"}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.6)
print(tokenizer.decode(outputs[0]))

📦 Downloads

File	Size	Description
`model.safetensors` (2 shards)	8.41 GB	Full BF16 precision (Transformers)

🧬 Architecture

Parameter	Value
Base	hotdogs/Agents-A1-4B-kimi-Preview
Base architecture	InternScience/Agents-A1-4B
Parameters	~4.29B
Hidden Size	2,560
Attention	Linear + Standard hybrid (8 full-attn layers)
Context	4,096 tokens (trained)
Precision	BF16
Format	ChatML (Jinja2 template)
Vocab	248,320 tokens

🏋️ Training Details

Fine-tuned from hotdogs/Agents-A1-4B-kimi-Preview using heretic.

Dataset: greghavens/kimi-k3-coding-and-debugging-traces — 3,389 clean coding agent traces, 3 epochs, scale=0.4.

Hyperparameter	Value
LoRA Rank	64
LoRA Alpha	128
Learning Rate	1e-4
Scheduler	Cosine (warmup 3%)
Merge Scale	0.4
Precision	BF16

✅ What This Model Excels At

Unrestricted responses — Refusal reduced to 37%
Coding agent tasks — Planning, debugging, building real projects
Step-by-step reasoning — Autonomous agent trace style
Tool calling — File operations, shell commands, testing
Multi-language — Python, C, C++, Go, Java, Rust, Bash, etc.
Multi-turn — Full trajectory conversations

⚠️ Disclaimer

This model is uncensored and may generate content that is offensive, harmful, or inappropriate. Use at your own risk. The authors are not responsible for any misuse.

💖 Support / โปรดสนับสนุน

If you find this model useful, please consider supporting my work!
หากคุณคิดว่าโมเดลนี้มีประโยชน์ กรุณาสนับสนุนผลงานของฉันด้วยนะคะ! 🙏

₿ Bitcoin — BTC:

bc1qf27cyk3vmugcdyv9xdtuv5jwz37863crpj5c9v

Thank you for your support! 🙏✨
ขอบคุณมากๆ สำหรับการสนับสนุนค่า! 💖🤗

🙏 Acknowledgements / ขอบคุณ

InternScience — For the Agents-A1-4B base model
greghavens — For the Kimi K3 coding traces dataset
p-e-w — For the heretic abliteration tool
Qwen Team (Alibaba) — For the Qwen3.5 architecture
Unsloth AI — For the training optimizations
All dataset contributors and the open-source AI community ❤️

Built with ❤️ by UKA — 18-year-old coder & cybersecurity expert