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saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic

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Magnet

MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic

A decensored variant of GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking, produced with Heretic v1.4.0 (directional ablation / "abliteration"). The base model is itself a V2 fine-tune of openbmb/MiniCPM5-1B on Fable 5 traces, focused on tool/function calling, coding, and instruction-following. Refusal behavior is suppressed via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the base model's knowledge and capabilities are left largely intact.

Who this is for: developers who want a tiny (1B) Thinking model with strong tool-calling and coding ability that answers directly instead of refusing — for local agents, roleplay, research on alignment/refusal mechanics, or any use case blocked by RLHF-era over-refusal. Runs comfortably on consumer GPUs and is small enough for on-device / edge deployment, while keeping MiniCPM5's 128K context and native Think / No-Think chat modes.

Part of RACER IS OP — Heretic Models — 38 decensored variants of open-weight LLMs.

Runs on your gaming PC

Full GGUF ladder included — pick the quant that fits your card:

| Your GPU | Recommended quant | Weights |

| :--- | :--- | :--- |

| RTX 3090 / 4090 / 5090 (24 GB) | Q8_0 | ~1.2 GB |

| RTX 4080 / 5080 / 4060 Ti 16G (16 GB) | Q6_K | ~0.9 GB |

| RTX 3060 / 4070 / 5070 (12 GB) | Q5_K_M | ~0.8 GB |

| RTX 4060 / 3070 (8 GB) | Q4_K_M | ~0.7 GB |

| GTX 1660 Super / 2060 / 3050 laptop (6 GB) | IQ4_XS | ~0.7 GB |

| CPU-only / Apple Silicon | Q4_K_M | fits in system RAM |

Weights only, at this model's 1.1B native size; add ~1 GB for context.

OOM? Drop one quant level. Headroom to spare? Go one up.

Why abliteration instead of fine-tuning

Fine-tuning a "helpful" persona on top of RLHF'd refusals fights the base model's training and tends to degrade coherence. Abliteration instead finds and edits the specific weight directions responsible for refusal, leaving the rest of the network (and its capabilities) untouched. See the Heretic repo and the original abliteration writeup for the mechanism.

Abliteration parameters

| Parameter | Value |

| :-------- | :---: |

| direction_index | 12.95 |

| attn.o_proj.max_weight | 1.14 |

| attn.o_proj.max_weight_position | 14.01 |

| attn.o_proj.min_weight | 0.99 |

| attn.o_proj.min_weight_distance | 12.84 |

| mlp.down_proj.max_weight | 0.98 |

| mlp.down_proj.max_weight_position | 14.20 |

| mlp.down_proj.min_weight | 0.39 |

| mlp.down_proj.min_weight_distance | 9.07 |

Performance

| Metric | This model | Original model (GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking) |

| :----- | :--------: | :---------------------------: |

| KL divergence | 0.0232 | 0 (by definition) |

| Refusals | 3/100 | 93/100 |

KL divergence of 0.0232 is very low — the edit is narrow and targeted rather than a broad perturbation. Refusals dropped from 93 to 3 out of 100 adversarial prompts while preserving the base model's tool-calling, coding, and thinking abilities.

Made with ❤️ by RACER IS OP — follow for more uncensored models

Files

GGUF quantizations

Full quantization set (14 quants + F16) produced with llama.cpp.

| File | Format | Size |

|---|---|---|

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-F16.gguf | GGUF F16 | 2.02 GB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q2_K.gguf | GGUF Q2_K | 463 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-IQ3_S.gguf | GGUF IQ3_S | 524 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q3_K_S.gguf | GGUF Q3_K_S | 523 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q3_K_M.gguf | GGUF Q3_K_M | 556 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q3_K_L.gguf | GGUF Q3_K_L | 585 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-IQ4_XS.gguf | GGUF IQ4_XS | 612 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q4_K_S.gguf | GGUF Q4_K_S | 637 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q4_0.gguf | GGUF Q4_0 | 634 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q4_1.gguf | GGUF Q4_1 | 687 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q4_K_M.gguf | GGUF Q4_K_M | 656 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q5_K_S.gguf | GGUF Q5_K_S | 739 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q5_K_M.gguf | GGUF Q5_K_M | 750 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q6_K.gguf | GGUF Q6_K | 851 MB |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q8_0.gguf | GGUF Q8_0 | 1.07 GB |

MiniCPM5 architecture — loads natively in llama.cpp / Ollama / LM Studio / Jan.

Run llama serve -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic to pull the default quant.

Quickstart

# llama.cpp
llama serve -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic
# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto",
)

messages = [{"role": "user", "content": "Write a Python function to merge two sorted lists."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Also runnable via Ollama, LM Studio, Jan, vLLM, SGLang — see the "Use this model" widget above for copy-paste commands. For tool/function calling, SGLang is the recommended backend; this model emits XML-style tool calls that SGLang's built-in minicpm5 parser converts to OpenAI-compatible tool_calls.

Responsible use

Refusal suppression is deliberate and works as intended: this model will comply with requests the base model would refuse, including some it shouldn't. There is no safety filtering layered on top. You are responsible for how you deploy it — don't put this behind an unmoderated public-facing endpoint serving third parties. It inherits this fine-tune's (and MiniCPM5-1B's) factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.

License

Inherits the Apache 2.0 license from the base model.

Related

Base model: GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking

Original model card (click to expand)

MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking

GGUF quantizations for local deployment: MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF

中文说明

MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking is a compact 1B Thinking language model built on openbmb/MiniCPM5-1B. Compared with V1, this V2 release is further fine-tuned on Fable 5 data with a stronger focus on tool calling / function calling, while also improving coding and instruction-following. It keeps MiniCPM5's native Thinking chat template and XML tool-call format.

Previous version: MiniCPM5-1B-Claude-Opus-Fable5-Thinking (V1)

For llama.cpp / Ollama / LM Studio deployment, see the GGUF repository.

Overview

| Item | Detail |

|---|---|

| Base model | openbmb/MiniCPM5-1B (1B dense Llama architecture) |

| Post-training | Fable 5 traces (V2) |

| Key gains vs V1 / base | Stronger tool calling, plus improved coding and instruction following |

| Chat format | MiniCPM5 native Thinking template with optional chain-of-thought blocks |

| Context length | 128K (max_position_embeddings = 131072) |

| Deployment | Single-GPU friendly; suitable for edge / local use |

Capabilities

  • Tool calling (enhanced in V2) — more reliable XML / function-calling style tool use on top of MiniCPM5's native format
  • Coding — code generation, debugging, and software-engineering-style tasks
  • Instruction following — more reliable adherence to user prompts and structured constraints
  • Thinking mode — chain-of-thought reasoning via the MiniCPM5 chat template
  • Long context — up to 128K tokens (131,072 tokens per config.json)

Benchmark

BFCL + API-Bank

| Model | BFCL non_live | BFCL live | API-Bank |

|---|---|---|---|

| MiniCPM5-1B (Base) | 41.51% | 60.24% | 7.30% |

| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking | 43.06% | 63.33% | 22.10% |

Tau-Bench

| Domain | MiniCPM5-1B (Base) | MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking |

|---|---|---|

| Airline | 0.34 (17/50) | 0.36 (18/50) |

| Retail | 0.052 (6/115) | 0.070 (8/115) |

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Write a Python function to merge two sorted lists."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Sampling recommendations

Generation defaults are inherited from MiniCPM5-1B:

| Mode | Params |

|---|---|

| Think (default) | temperature=0.9, top_p=0.95 |

| No Think | temperature=0.7, top_p=0.95, enable_thinking=False |

Limitations

  • Thinking outputs — the model may emit reasoning blocks before the final answer; downstream apps can strip them before display
  • 1B scale — optimized for lightweight local deployment, not frontier-scale general reasoning

Provenance & licensing

Released under Apache-2.0, inherited from MiniCPM5-1B.

Acknowledgements