Instructions to use r2rss/Malachite-7b-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use r2rss/Malachite-7b-v0 with Transformers:

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

pipe = pipeline("text-generation", model="r2rss/Malachite-7b-v0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("r2rss/Malachite-7b-v0")
model = AutoModelForMultimodalLM.from_pretrained("r2rss/Malachite-7b-v0")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use r2rss/Malachite-7b-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "r2rss/Malachite-7b-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "r2rss/Malachite-7b-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/r2rss/Malachite-7b-v0

SGLang

How to use r2rss/Malachite-7b-v0 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 "r2rss/Malachite-7b-v0" \
    --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": "r2rss/Malachite-7b-v0",
		"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 "r2rss/Malachite-7b-v0" \
        --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": "r2rss/Malachite-7b-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use r2rss/Malachite-7b-v0 with Docker Model Runner:
```
docker model run hf.co/r2rss/Malachite-7b-v0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Malachite-7b-v0

This model is a merge of the following models made with mergekit:

🧩 Configuration

slices:
  - sources:
      - model: zyh3826/GML-Mistral-merged-v1
        layer_range: [0, 32]
      - model: cookinai/CatMacaroni-Slerp
        layer_range: [0, 32]
merge_method: slerp
base_model: cookinai/CatMacaroni-Slerp
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for r2rss/Malachite-7b-v0

Quantizations

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