Instructions to use Skttttt/Himeros-V2-27B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Skttttt/Himeros-V2-27B-GGUF 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 Skttttt/Himeros-V2-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Skttttt/Himeros-V2-27B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Skttttt/Himeros-V2-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Skttttt/Himeros-V2-27B-GGUF: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 Skttttt/Himeros-V2-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Skttttt/Himeros-V2-27B-GGUF: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 Skttttt/Himeros-V2-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Skttttt/Himeros-V2-27B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Skttttt/Himeros-V2-27B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Skttttt/Himeros-V2-27B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Skttttt/Himeros-V2-27B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Skttttt/Himeros-V2-27B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Skttttt/Himeros-V2-27B-GGUF:Q4_K_M

Ollama
How to use Skttttt/Himeros-V2-27B-GGUF with Ollama:
```
ollama run hf.co/Skttttt/Himeros-V2-27B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Skttttt/Himeros-V2-27B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Skttttt/Himeros-V2-27B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Skttttt/Himeros-V2-27B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Skttttt/Himeros-V2-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/Skttttt/Himeros-V2-27B-GGUF:Q4_K_M
```

Lemonade

How to use Skttttt/Himeros-V2-27B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Skttttt/Himeros-V2-27B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Himeros-V2-27B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Skttttt/Himeros-V2-27B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Skttttt/Himeros-V2-27B-GGUF: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 Skttttt/Himeros-V2-27B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Skttttt/Himeros-V2-27B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Skttttt/Himeros-V2-27B-GGUF: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 "Skttttt/Himeros-V2-27B-GGUF: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"

Himeros V2 27B · Long-context roleplay

Character-driven dialogue, romance and long-form creative writing

Himeros V2 is a 27B LoRA fine-tune of Qwen3.8-27B-Uncensored, trained with sequences up to 49,152 tokens. Its focus is English roleplay: sustained scenes, character interaction and narrative continuation.

This repository distributes GGUF precision variants of the same merged model. Each GGUF is standalone: download one file, with no separate adapter or base-model download required.

Model overview

Detail	Configuration
Base model	orcarouter/Qwen3.8-27B-Uncensored
Base revision	`404ea47aaa5d8a8b00049c9e9750089aca011ab2`
Model size	27B parameter class
Primary language	English
Fine-tuning method	LoRA, merged into the base before export
LoRA rank / alpha	16 / 32
Learning rate / schedule	1e-5 / cosine
Training sequence limit	49,152 tokens (48 × 1,024)
Distribution	Single-file GGUF, K and IQ quantizations plus BF16

The training mixture combines roleplay conversations, synthetic dialogue and long-form prose. Source training texts are not included in this repository. The training context limit describes the fine-tuning configuration; it does not guarantee uniform quality across an entire 48K conversation.

GGUF collection

The export set comprises the following 23 formats. Consult Files and versions for files currently available; UPLOAD_COMPLETE.json records completion of the full transfer.

Family	Formats
K quantizations	Q2_K, Q2_K_S, Q3_K_S, Q3_K_M, Q3_K_L, Q4_K_S, Q4_K_M, Q5_K_S, Q5_K_M, Q6_K
IQ quantizations	IQ1_S, IQ1_M, IQ2_XXS, IQ2_XS, IQ2_S, IQ2_M, IQ3_XXS, IQ3_XS, IQ3_S, IQ3_M, IQ4_NL, IQ4_XS
Unquantized export	BF16

Files follow this naming pattern:

Himeros-V2-27B-<FORMAT>.gguf

These are alternatives, not pieces of one model. Choose a precision that fits your available memory, leaving room for the context cache and runtime overhead. Quantization can affect output quality; this release does not provide a controlled comparison of every format.

Getting started

Download a single .gguf from Files and versions.
Import it into a compatible LM Studio or llama.cpp runtime.
Use the embedded chat template.
Set a context length and GPU offload configuration that fit your hardware.
Supply your character description, scene setup and writing preferences in the system prompt.

Thinking mode

Thinking is not permanently disabled in the GGUF. Whether it is enabled depends on the client and inference backend.

A short local LM Studio test confirmed zero reasoning tokens with Off and reasoning output with Low. This verifies the tested integration, not identical behavior in every client. Exact Low/Medium/High budgets remain backend-dependent.

Export integrity

The original quantization shards were joined losslessly and audited for unchanged tensor names, shapes, types and raw tensor bytes. The transfer workflow compares file SHA-256 against the completed Drive export record and Hugging Face file metadata.

SHA256SUMS.txt provides download checksums once the full transfer completes. These checks verify file integrity, not generation quality. JSON completion records are not required to load a GGUF.

Evaluation and limitations

No controlled benchmark establishes superiority over the base model.
Grammar, character continuity, repetition and long-context recall can still fail.
Quantized variants have not all been independently quality-benchmarked.
Outputs may contain mature themes, bias or inaccurate information; review them for your use case.
This is a creative-writing model, not a source of factual or professional advice.

Attribution and licensing

Thanks to the upstream model authors and the developers of the training and GGUF tooling. Base-model use and redistribution remain subject to the applicable upstream terms. This card does not assert an additional license grant or that third-party training-source rights have been cleared.

Questions

If you have any questions, please DM me.

Downloads last month: 14,111

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Skttttt/Himeros-V2-27B-GGUF

Base model

Qwen/Qwen3.8-27B

Finetuned

orcarouter/Qwen3.8-27B-Uncensored

Quantized

(41)

this model

Collection including Skttttt/Himeros-V2-27B-GGUF

Himeros

Collection

A collection of all versions of Himeros models. Note that version 1 has really low quality • 3 items • Updated 4 days ago