Instructions to use lemonilia/AshhLimaRP-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lemonilia/AshhLimaRP-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="lemonilia/AshhLimaRP-Mistral-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lemonilia/AshhLimaRP-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("lemonilia/AshhLimaRP-Mistral-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lemonilia/AshhLimaRP-Mistral-7B 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 lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lemonilia/AshhLimaRP-Mistral-7B: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 lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lemonilia/AshhLimaRP-Mistral-7B: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 lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M

Use Docker

docker model run hf.co/lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M

LM Studio
Jan

vLLM

How to use lemonilia/AshhLimaRP-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lemonilia/AshhLimaRP-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemonilia/AshhLimaRP-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M

SGLang

How to use lemonilia/AshhLimaRP-Mistral-7B 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 "lemonilia/AshhLimaRP-Mistral-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemonilia/AshhLimaRP-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "lemonilia/AshhLimaRP-Mistral-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemonilia/AshhLimaRP-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use lemonilia/AshhLimaRP-Mistral-7B with Ollama:
```
ollama run hf.co/lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M
```

Unsloth Studio

How to use lemonilia/AshhLimaRP-Mistral-7B 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 lemonilia/AshhLimaRP-Mistral-7B 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 lemonilia/AshhLimaRP-Mistral-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lemonilia/AshhLimaRP-Mistral-7B to start chatting

Atomic Chat new
Docker Model Runner
How to use lemonilia/AshhLimaRP-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M
```

Lemonade

How to use lemonilia/AshhLimaRP-Mistral-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lemonilia/AshhLimaRP-Mistral-7B:Q4_K_M

Run and chat with the model

lemonade run user.AshhLimaRP-Mistral-7B-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.

AshhLimaRP-Mistral-7B (Alpaca, v1)

This is a version of LimaRP with 2000 training samples up to about 9k tokens length finetuned on Ashhwriter-Mistral-7B.

LimaRP is a longform-oriented, novel-style roleplaying chat model intended to replicate the experience of 1-on-1 roleplay on Internet forums. Short-form, IRC/Discord-style RP (aka "Markdown format") is not supported. The model does not include instruction tuning, only manually picked and slightly edited RP conversations with persona and scenario data.

Ashhwriter, the base, is a model entirely finetuned on human-written lewd stories.

Available versions

Float16 HF weights
LoRA Adapter (adapter_config.json and adapter_model.bin)
4bit AWQ
Q4_K_M GGUF
Q6_K GGUF

Prompt format

Extended Alpaca format, with ### Instruction:, ### Input: immediately preceding user inputs and ### Response: immediately preceding model outputs. While Alpaca wasn't originally intended for multi-turn responses, in practice this is not a problem; the format follows a pattern already used by other models.

### Instruction:
Character's Persona: {bot character description}

User's Persona: {user character description}

Scenario: {what happens in the story}

Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User.

### Input:
User: {utterance}

### Response:
Character: {utterance}

### Input
User: {utterance}

### Response:
Character: {utterance}

(etc.)

You should:

Replace all text in curly braces (curly braces included) with your own text.
Replace User and Character with appropriate names.

Message length control

Inspired by the previously named "Roleplay" preset in SillyTavern, with this version of LimaRP it is possible to append a length modifier to the response instruction sequence, like this:

### Input
User: {utterance}

### Response: (length = medium)
Character: {utterance}

This has an immediately noticeable effect on bot responses. The lengths using during training are: micro, tiny, short, medium, long, massive, huge, enormous, humongous, unlimited. The recommended starting length is medium. Keep in mind that the AI can ramble or impersonate the user with very long messages.

The length control effect is reproducible, but the messages will not necessarily follow lengths very precisely, rather follow certain ranges on average, as seen in this table with data from tests made with one reply at the beginning of the conversation:

Response length control appears to work well also deep into the conversation. By omitting the modifier, the model will choose the most appropriate response length (although it might not necessarily be what the user desires).

Suggested settings

You can follow these instruction format settings in SillyTavern. Replace medium with your desired response length:

Text generation settings

These settings could be a good general starting point:

TFS = 0.90
Temperature = 0.70
Repetition penalty = ~1.11
Repetition penalty range = ~2048
top-k = 0 (disabled)
top-p = 1 (disabled)

Training procedure

Axolotl was used for training on 2x NVidia A40 GPUs.

The A40 GPUs have been graciously provided by Arc Compute.

Training hyperparameters

A lower learning rate than usual was employed. Due to an unforeseen issue the training was cut short and as a result 3 epochs were trained instead of the planned 4. Using 2 GPUs, the effective global batch size would have been 16.

Training was continued from the most recent LoRA adapter from Ashhwriter, using the same LoRA R and LoRA alpha.