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

Raw

History Blame Contribute Delete

7.41 kB

metadata

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
            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
            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 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

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

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

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:

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 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"

./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