Instructions to use saidutta69/EuroLLM-9B-Instruct-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use saidutta69/EuroLLM-9B-Instruct-heretic with Transformers:

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

pipe = pipeline("text-generation", model="saidutta69/EuroLLM-9B-Instruct-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("saidutta69/EuroLLM-9B-Instruct-heretic")
model = AutoModelForCausalLM.from_pretrained("saidutta69/EuroLLM-9B-Instruct-heretic", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use saidutta69/EuroLLM-9B-Instruct-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/EuroLLM-9B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/EuroLLM-9B-Instruct-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/EuroLLM-9B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf saidutta69/EuroLLM-9B-Instruct-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/EuroLLM-9B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf saidutta69/EuroLLM-9B-Instruct-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/EuroLLM-9B-Instruct-heretic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf saidutta69/EuroLLM-9B-Instruct-heretic:Q4_K_M

Use Docker

docker model run hf.co/saidutta69/EuroLLM-9B-Instruct-heretic:Q4_K_M

LM Studio
Jan

vLLM

How to use saidutta69/EuroLLM-9B-Instruct-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "saidutta69/EuroLLM-9B-Instruct-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/EuroLLM-9B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/saidutta69/EuroLLM-9B-Instruct-heretic:Q4_K_M

SGLang

How to use saidutta69/EuroLLM-9B-Instruct-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/EuroLLM-9B-Instruct-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/EuroLLM-9B-Instruct-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/EuroLLM-9B-Instruct-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/EuroLLM-9B-Instruct-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use saidutta69/EuroLLM-9B-Instruct-heretic with Ollama:
```
ollama run hf.co/saidutta69/EuroLLM-9B-Instruct-heretic:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use saidutta69/EuroLLM-9B-Instruct-heretic with Docker Model Runner:
```
docker model run hf.co/saidutta69/EuroLLM-9B-Instruct-heretic:Q4_K_M
```

Lemonade

How to use saidutta69/EuroLLM-9B-Instruct-heretic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull saidutta69/EuroLLM-9B-Instruct-heretic:Q4_K_M

Run and chat with the model

lemonade run user.EuroLLM-9B-Instruct-heretic-Q4_K_M

List all available models

lemonade list

Atomic Chat

EuroLLM-9B-Instruct-heretic

A decensored variant of utter-project/EuroLLM-9B-Instruct, produced with Heretic v1.4.0 (directional ablation / "abliteration"). The EU's flagship multilingual open model keeps all 35 of its languages, its instruction tuning and its machine-translation training; refusal behaviour is suppressed via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning.

Who this is for: anyone who needs a model that actually works in European languages - or in Arabic, Chinese, Hindi, Japanese, Korean or Russian - rather than translating everything through English first. EuroLLM-9B was trained on 4 trillion tokens spread across those languages, including parallel en-xx and xx-en data, and EuroBlocks instruction tuning targeted general instruction-following and machine translation specifically. Refusals drop from 100/100 to 4/100. Use it for multilingual assistants, translation, and EU-language compliance work on a single 16 GB GPU.

Context length is only 4,096 tokens. This model's pretrained sequence length is 4,096 and its config ships no rope-scaling or YaRN settings, so the GGUF loads with 4,096 as the context limit and nothing more. It is the shortest-context model in this collection. If you need long documents, pick something else.

Runs on your gaming PC

Your GPU	Recommended quant	Weights
RTX 3090 / 4090 / 5090 (24 GB)	Q8_0	9.73 GB
RTX 4080 / 5080 (16 GB)	Q6_K	7.51 GB
A6000 / A100 40GB (40 GB)	Q8_0	9.73 GB
CPU-only (32 GB RAM)	Q4_K_M	5.58 GB

Weights only, at this model's 9.15B native size. At 9B this needs a real GPU - it will run on CPU but not quickly. Note the short context: KV cache is small, so this model is compute-bound rather than memory-bound on long generations.

Abliteration parameters

Trial 100 of a 200-trial Heretic run (seed 2494858073).

Parameter	Value
direction_index	23.66
attn.o_proj.max_weight	0.88
attn.o_proj.max_weight_position	27.67
attn.o_proj.min_weight	0.83
attn.o_proj.min_weight_distance	18.96
mlp.down_proj.max_weight	1.16
mlp.down_proj.max_weight_position	35.07
mlp.down_proj.min_weight	1.12
mlp.down_proj.min_weight_distance	23.46

Performance

Metric	This model	Original model (utter-project/EuroLLM-9B-Instruct)
KL divergence	0.0383	0 (by definition)
Refusals	4/100	100/100

The base model refused every single prompt in the harmful evaluation set - 100/100, the most complete refusal tuning of any model in this collection - and that is reduced to 4/100. The KL divergence of 0.0383 is a moderate edit: it sits between the very tight runs (Qwen3-4B-Thinking-heretic at 0.0011) and the deliberately loose one (granite-4.1-3b-heretic at 0.0852), so expect some drift in phrasing alongside the refusals disappearing.

Base model results

Abliteration does not add capability. The base model's evaluation tables are published as images, so only the summary claims are quotable here - no invented numbers.

Multilingual: EuroLLM-9B takes a Borda count of 1.0 (best average rank) among European-developed open-weight models, and is reported competitive with non-European models - broadly on par with Gemma-2-9B while beating most others on most benchmarks. Evaluated with Okapi datasets (11 languages) for ARC-Challenge, HellaSwag and MMLU, and with Tower-translated MMLU-Pro / MUSR for 6 EU languages.
English: reported to surpass most European-developed open-weight models and to match Mistral-7B on Borda count.
No post-abliteration eval has been run on any of the above.

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.

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

Files

Safetensors

File	Size
`model-00001-of-00004.safetensors`	5.00 GB
`model-00002-of-00004.safetensors`	4.92 GB
`model-00003-of-00004.safetensors`	4.92 GB
`model-00004-of-00004.safetensors`	3.47 GB

BF16, 9,152,319,488 parameters (9.15B), with untied input/output embeddings (~0.52B each). The reproduce/ directory carries the full Heretic recipe - config.toml, requirements.txt, the Optuna study journal, and SHA-256 sums - so this exact model can be regenerated bit-for-bit. Reproduce it with heretic --reproduce reproduce/reproduce.json.

GGUF quantizations

Default set (F16 + 4 quants) produced with llama.cpp.

File	Format	Size
`EuroLLM-9B-Instruct-heretic-F16.gguf`	GGUF F16	18.31 GB
`EuroLLM-9B-Instruct-heretic-Q4_K_M.gguf`	GGUF Q4_K_M	5.58 GB
`EuroLLM-9B-Instruct-heretic-Q5_K_M.gguf`	GGUF Q5_K_M	6.52 GB
`EuroLLM-9B-Instruct-heretic-Q6_K.gguf`	GGUF Q6_K	7.51 GB
`EuroLLM-9B-Instruct-heretic-Q8_0.gguf`	GGUF Q8_0	9.73 GB

Llama architecture (llama, 381 tensors, 42 layers) - loads natively in llama.cpp / Ollama / LM Studio / Jan, and is a standard dense model so MLX and vLLM work too.

Run llama serve -hf saidutta69/EuroLLM-9B-Instruct-heretic to pull the default quant.

Quickstart

# llama.cpp -- override the short native context if you have the RAM
llama serve -hf saidutta69/EuroLLM-9B-Instruct-heretic -c 8192

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "saidutta69/EuroLLM-9B-Instruct-heretic"
model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [
    {"role": "system",
      "content": "You are EuroLLM --- an AI assistant specialized in European languages."},
    {"role": "user", "content": "Translate to Polish: 'The library closes at six.' Also answer in English: what is the capital of Portugal?"},
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,
                                       return_dict=True, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

Abliteration does not touch the tokenizer, so the base model's multilingual setup is intact. Note the system prompt above is the one the base model card recommends - keep a language hint in it if you want reliable language selection.

Model details


Architecture	`LlamaForCausalLM` (decoder-only dense transformer)
Parameters	9.15B (0.52B embeddings + 0.52B LM head + 8.10B non-embedding)
Layers / heads	42 layers, 32 attention heads, 8 KV heads (GQA), head_dim 128
Hidden / intermediate	4096 / 12288 (SwiGLU, pre-layer RMSNorm)
Position embedding	RoPE, theta = 10,000
Context length	4,096 native - no rope_scaling in the config
Vocab	128,000
Precision	bfloat16, untied embeddings
Languages	Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Arabic, Catalan, Chinese, Galician, Hindi, Japanese, Korean, Norwegian, Russian, Turkish, Ukrainian
Developers	Unbabel, Instituto Superior Tecnico, Instituto de Telecomunicacoes, University of Edinburgh, Aveni, University of Paris-Saclay, University of Amsterdam, Naver Labs, Sorbonne Universite - funded by the European Union
Training	4 trillion tokens across the language set; 400 NVIDIA H100s on the Marenostrum 5 supercomputer; instruction-tuned on EuroBlocks
Base model	utter-project/EuroLLM-9B-Instruct (a post-trained utter-project/EuroLLM-9B)

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 EuroLLM's factual limitations and biases across all 35 languages; abliteration removes refusal directions, it doesn't add capability or judgment.

Because this model is strong at translation into and out of many languages, treat it accordingly: unfiltered translation of untrusted content is a good way to launder text past another system's filters.

License

Inherits the apache-2.0 license from the base model, which is tagged Apache-2.0 on the Hub but ships no LICENSE file, so the canonical license text is linked rather than a repo blob.

utter-project/EuroLLM-9B-Instruct - the base model (gated - request access there)
utter-project/EuroLLM-9B - the pre-trained, non-instruct version
EuroLLM project - project site and the rest of the model family
RACER IS OP - Heretic Models - full collection

Downloads last month: 556

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for saidutta69/EuroLLM-9B-Instruct-heretic

Base model

utter-project/EuroLLM-9B

Finetuned

utter-project/EuroLLM-9B-Instruct

Quantized

(16)

this model

Quantizations

2 models

Collection including saidutta69/EuroLLM-9B-Instruct-heretic

RACER IS OP — Heretic Models

Collection

All abliterated/heretic models by RACER IS OP — uncensored variants of open-weight LLMs • 67 items • Updated about 20 hours ago • 7

saidutta69
/

EuroLLM-9B-Instruct-heretic

EuroLLM-9B-Instruct-heretic

Runs on your gaming PC

Abliteration parameters

Performance

Base model results

Why abliteration instead of fine-tuning

Files

Safetensors

GGUF quantizations

Quickstart

Model details

Responsible use

License

Related

Model tree for saidutta69/EuroLLM-9B-Instruct-heretic

Collection including saidutta69/EuroLLM-9B-Instruct-heretic

RACER IS OP — Heretic Models