Instructions to use QuantFactory/calme-2.1-phi3.5-4b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/calme-2.1-phi3.5-4b-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/calme-2.1-phi3.5-4b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/calme-2.1-phi3.5-4b-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/calme-2.1-phi3.5-4b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/calme-2.1-phi3.5-4b-GGUF",
	filename="calme-2.1-phi3.5-4b.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/calme-2.1-phi3.5-4b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/calme-2.1-phi3.5-4b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/calme-2.1-phi3.5-4b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/calme-2.1-phi3.5-4b-GGUF:Q4_K_M

SGLang

How to use QuantFactory/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/calme-2.1-phi3.5-4b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/calme-2.1-phi3.5-4b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-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/calme-2.1-phi3.5-4b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/calme-2.1-phi3.5-4b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/calme-2.1-phi3.5-4b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/calme-2.1-phi3.5-4b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/calme-2.1-phi3.5-4b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.calme-2.1-phi3.5-4b-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/calme-2.1-phi3.5-4b-GGUF

This is quantized version of MaziyarPanahi/calme-2.1-phi3.5-4b created using llama.cpp

Original Model Card

MaziyarPanahi/calme-2.1-phi3.5-4b

This model is a fine-tuned version of the microsoft/Phi-3.5-mini-instruct, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications.

Use Cases

This model is suitable for a wide range of applications, including but not limited to:

Advanced question-answering systems
Intelligent chatbots and virtual assistants
Content generation and summarization
Code generation and analysis
Complex problem-solving and decision support

⚡ Quantized GGUF

Here are the quants: calme-2.1-phi3.5-4b-GGUF

🏆 Open LLM Leaderboard Evaluation Results

Coming soon!

Prompt Template

This model uses ChatML prompt template:

<|system|>
You are a helpful assistant.<|end|>
<|user|>
How to explain Internet for a medieval knight?<|end|>
<|assistant|>

How to use


# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.1-phi3.5-4b")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-phi3.5-4b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-phi3.5-4b")

Ethical Considerations

As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	27.01
IFEval (0-Shot)	56.59
BBH (3-Shot)	36.11
MATH Lvl 5 (4-Shot)	14.43
GPQA (0-shot)	12.53
MuSR (0-shot)	9.77
MMLU-PRO (5-shot)	32.61

Downloads last month: 535

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/calme-2.1-phi3.5-4b-GGUF

Base model

microsoft/Phi-3.5-mini-instruct

Quantized

(183)

this model

Dataset used to train QuantFactory/calme-2.1-phi3.5-4b-GGUF

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

56.590
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

36.110
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

14.430
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

12.530
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

9.770
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

32.610