Instructions to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic")
model = AutoModelForCausalLM.from_pretrained("saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic",
	filename="MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

Use Docker

docker model run hf.co/saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

LM Studio
Jan

vLLM

How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

SGLang

How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Ollama:
```
ollama run hf.co/saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
```

Unsloth Studio

How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic to start chatting

How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

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 saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

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 "saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Docker Model Runner:
```
docker model run hf.co/saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M
```

Lemonade

How to use saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saidutta69/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q4_K_M

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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.

This model is reproducible!

See the README in the reproduce directory for the exact config, full parameter/metric dump, evaluation transcripts, and SHA256 checksums.

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

File	Format	Size
`model.safetensors`	BF16	~2.2 GB
`MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q8_0.gguf`	GGUF, Q8_0	1.10 GB
`MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q5_K_M.gguf`	GGUF, Q5_K_M	751 MB
`MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-heretic-Q4_K_M.gguf`	GGUF, Q4_K_M	656 MB
`reproduce/`	Config + eval transcripts + checksums	—

GGUF quants are produced with llama.cpp (MiniCPM5 uses the standard LlamaForCausalLM architecture, so it loads in llama.cpp / Ollama / LM Studio / Jan directly). 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.

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

Base model: OpenBMB / MiniCPM5-1B
GGUF conversion: llama.cpp

Downloads last month: 1,621

Safetensors

Model size

1B params

Tensor type

BF16

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

Base model

openbmb/MiniCPM5-1B

Finetuned

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

Finetuned

(2)

this model

Quantizations

1 model

saidutta69
/

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

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

Why abliteration instead of fine-tuning

Abliteration parameters

Performance

Files

Quickstart

Responsible use

License

Related

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

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

Overview

Capabilities

Benchmark

BFCL + API-Bank

Tau-Bench

Quick start

Sampling recommendations

Limitations

Provenance & licensing

Acknowledgements

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