Instructions to use Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Netsnake/Refact-1_6B-fim-Q5_K_M-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 Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_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 Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_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 Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M

Use Docker

docker model run hf.co/Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M

LM Studio
Jan

vLLM

How to use Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M

SGLang

How to use Netsnake/Refact-1_6B-fim-Q5_K_M-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 "Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF" \
    --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": "Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF",
		"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 "Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF" \
        --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": "Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF with Ollama:
```
ollama run hf.co/Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M
```

Unsloth Studio

How to use Netsnake/Refact-1_6B-fim-Q5_K_M-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 Netsnake/Refact-1_6B-fim-Q5_K_M-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 Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M
```

Lemonade

How to use Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF:Q5_K_M

Run and chat with the model

lemonade run user.Refact-1_6B-fim-Q5_K_M-GGUF-Q5_K_M

List all available models

lemonade list

Refact-1_6B-fim-Q5_K_M-GGUF / README.md

Netsnake

Upload README.md with huggingface_hub

2ecc144 verified 11 months ago

preview code

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	---
	pipeline_tag: text-generation
	inference: true
	widget:
	- text: 'def print_hello_world():'
	example_title: Hello world
	group: Python
	license: bigscience-openrail-m
	pretrain-datasets:
	- books
	- arxiv
	- c4
	- falcon-refinedweb
	- wiki
	- github-issues
	- stack_markdown
	- self-made dataset of permissive github code
	datasets:
	- bigcode/the-stack-dedup
	- rombodawg/2XUNCENSORED_MegaCodeTraining188k
	- bigcode/commitpackft
	metrics:
	- code_eval
	library_name: transformers
	tags:
	- code
	- llama-cpp
	- gguf-my-repo
	language:
	- en
	base_model: refactai/Refact-1_6B-fim
	model-index:
	- name: Refact-1.6B
	results:
	- task:
	type: text-generation
	dataset:
	name: HumanEval
	type: openai_humaneval
	metrics:
	- type: pass@1
	value: 32.0
	name: pass@1 (T=0.01)
	verified: false
	- type: pass@1
	value: 31.5
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@10
	value: 53.0
	name: pass@10 (T=0.8)
	verified: false
	- type: pass@100
	value: 76.9
	name: pass@100 (T=0.8)
	verified: false
	- task:
	type: text-generation
	dataset:
	name: HumanEvalSynthesize Python
	type: bigcode/humanevalpack
	metrics:
	- type: pass@1
	value: 35.8
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 31.6
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 29.1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: -1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 26.3
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: -1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: -1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 18.38
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 12.28
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 15.12
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: -1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 13.17
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 2.8
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: -1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 26.92
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 26.85
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 30.76
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: -1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 25.94
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 8.44
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: -1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 26.46
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 17.86
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 20.94
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: -1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 18.78
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: -1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: -1
	name: pass@1 (T=0.2)
	verified: false
	- task:
	type: text-generation
	dataset:
	name: MBPP
	type: mbpp
	metrics:
	- type: pass@1
	value: 31.15
	name: pass@1 (T=0.01)
	verified: false
	- task:
	type: text-generation
	dataset:
	name: DS-1000 (Overall Completion)
	type: ds1000
	metrics:
	- type: pass@1
	value: 10.1
	name: pass@1 (T=0.2)
	verified: false
	- task:
	type: text-generation
	dataset:
	name: MultiPL-HumanEval (C++)
	type: nuprl/MultiPL-E
	metrics:
	- type: pass@1
	value: 21.61
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 13.91
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 9.5
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 53.57
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 21.58
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 13.75
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 26.88
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 15.26
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 23.04
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 12.1
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 29.6
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 13.77
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 12.68
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 4.29
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 19.54
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 18.33
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 5.7
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 17.68
	name: pass@1 (T=0.2)
	verified: false
	- type: pass@1
	value: 25
	name: pass@1 (T=0.2)
	verified: false
	---

	# Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF
	This model was converted to GGUF format from [`refactai/Refact-1_6B-fim`](https://huggingface.co/refactai/Refact-1_6B-fim) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
	Refer to the [original model card](https://huggingface.co/refactai/Refact-1_6B-fim) for more details on the model.

	## Use with llama.cpp
	Install llama.cpp through brew (works on Mac and Linux)

	```bash
	brew install llama.cpp

	```
	Invoke the llama.cpp server or the CLI.

	### CLI:
	```bash
	llama-cli --hf-repo Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF --hf-file refact-1_6b-fim-q5_k_m.gguf -p "The meaning to life and the universe is"
	```

	### Server:
	```bash
	llama-server --hf-repo Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF --hf-file refact-1_6b-fim-q5_k_m.gguf -c 2048
	```

	Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

	Step 1: Clone llama.cpp from GitHub.
	```
	git clone https://github.com/ggerganov/llama.cpp
	```

	Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
	```
	cd llama.cpp && LLAMA_CURL=1 make
	```

	Step 3: Run inference through the main binary.
	```
	./llama-cli --hf-repo Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF --hf-file refact-1_6b-fim-q5_k_m.gguf -p "The meaning to life and the universe is"
	```
	or
	```
	./llama-server --hf-repo Netsnake/Refact-1_6B-fim-Q5_K_M-GGUF --hf-file refact-1_6b-fim-q5_k_m.gguf -c 2048
	```