Instructions to use QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF",
	filename="karakuri-lm-7b-apm-v0.2.Q2_K.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 QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/karakuri-lm-7b-apm-v0.2-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 QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/karakuri-lm-7b-apm-v0.2-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 QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/karakuri-lm-7b-apm-v0.2-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": "QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M

SGLang

How to use QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF 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 "QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF" \
    --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": "QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF",
		"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 "QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF" \
        --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": "QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF 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 QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF 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 QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.karakuri-lm-7b-apm-v0.2-GGUF-Q4_K_M

List all available models

lemonade list

KARAKURI LM 7B APM v0.2- GGUF

This is quantized version of karakuri-ai/karakuri-lm-7b-apm-v0.2 created using llama.cpp

Model Details

Model Description

Developed by: KARAKURI Inc.
Model type: Causal decoder-only transformer language model
Languages: Primarily English
License: Apache 2.0
Finetuned from model: mistral-community/Mistral-7B-v0.2
Contact: For questions and comments about the model, please email karakuri-rd@karakuri.ai

Usage

KARAKURI LM 7B APM v0.2 is a attribute prediction model that rates model responses on various aspects that makes a response desirable.

Given a conversation with multiple turns between user and assistant, the model rates the following attributes (between 0 and 4) for every assistant turn.

helpfulness: Overall helpfulness of the response to the prompt.
correctness: Inclusion of all pertinent facts without errors.
coherence: Consistency and clarity of expression.
complexity: Intellectual depth required to write response (i.e. whether the response can be written by anyone with basic language competency or requires deep domain expertise).
verbosity: Amount of detail included in the response, relative to what is asked for in the prompt.
quality: Perceived goodness of response.
toxicity: Undesirable elements such as vulgar, harmful or potentially biased response.
humor: Sense of humor within response.
creativity: Willingness to generate non-conventional response.

The first five are derived from HelpSteer, while the remaining four are derived from OASST2.

You can run the model using the 🤗 Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "karakuri-ai/karakuri-lm-7b-apm-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Hello!"},
    {"role": "assistant", "content": "Hello! How can I help you today?"},
]
tokenizer.apply_chat_template(
    messages,
    label="helpsteer",
    tokenize=False,
    add_generation_prompt=True,
)
# <bos>[INST] Hello! [/INST] Hello! How can I help you today? [ATTR_1]

input_ids = tokenizer.apply_chat_template(
    messages,
    label="helpsteer",
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)
outputs = model.generate(input_ids, max_new_tokens=32)
tokenizer.decode(outputs[0][input_ids.shape[-1]:])
#  helpfulness: 2 correctness: 1 coherence: 2 complexity: 1 verbosity: 1 [/ATTR_1]<eos>

messages += [
    {"role": "label", "content": "helpfulness: 2 correctness: 1 coherence: 2 complexity: 1 verbosity: 1"},
    {"role": "user", "content": "Thank you!"},
    {"role": "assistant", "content": "You're welcome! I'm happy to help however I can."},
]
tokenizer.apply_chat_template(
    messages,
    label="helpsteer",
    tokenize=False,
    add_generation_prompt=True,
)
# <bos>[INST] Hello! [/INST] Hello! How can I help you today? [ATTR_1] helpfulness: 2 correctness: 1 coherence: 2 complexity: 1 verbosity: 1 [/ATTR_1]<eos>[INST] Thank you! [/INST] You're welcome! I'm happy to help however I can. [ATTR_1]

messages = [
    {"role": "user", "content": "Hello!"},
    {"role": "assistant", "content": "Hello! How can I help you today?"},
]
tokenizer.apply_chat_template(
    messages,
    label="oasst",
    tokenize=False,
    add_generation_prompt=True,
)
# <bos>[INST] Hello! [/INST] Hello! How can I help you today? [ATTR_2]

input_ids = tokenizer.apply_chat_template(
    messages,
    label="oasst",
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)
outputs = model.generate(input_ids, max_new_tokens=32)
tokenizer.decode(outputs[0][input_ids.shape[-1]:])
#  quality: 3 toxicity: 1 humor: 1 creativity: 1 [/ATTR_2]<eos>

Training Details

Training Data

Training Infrastructure

Hardware: The model was trained on single node of an Amazon EC2 trn1.32xlarge instance.
Software: We use code based on neuronx-nemo-megatron.

Model Citation

@misc{karakuri_lm_7b_apm_v02,
    author       = { {KARAKURI} {I}nc. },
    title        = { {KARAKURI} {LM} 7{B} {APM} v0.2 },
    year         = { 2024 },
    url          = { https://huggingface.co/karakuri-ai/karakuri-lm-7b-apm-v0.2 },
    publisher    = { Hugging Face },
    journal      = { Hugging Face repository }
}

Downloads last month: 234

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/karakuri-lm-7b-apm-v0.2-GGUF

Base model

mistral-community/Mistral-7B-v0.2

Finetuned

karakuri-ai/karakuri-lm-7b-apm-v0.2

Quantized

(1)

this model

QuantFactory
/

karakuri-lm-7b-apm-v0.2-GGUF