Instructions to use zelus82/Panoramix-7B-Instruct-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zelus82/Panoramix-7B-Instruct-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="zelus82/Panoramix-7B-Instruct-v0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zelus82/Panoramix-7B-Instruct-v0.2")
model = AutoModelForCausalLM.from_pretrained("zelus82/Panoramix-7B-Instruct-v0.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zelus82/Panoramix-7B-Instruct-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zelus82/Panoramix-7B-Instruct-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zelus82/Panoramix-7B-Instruct-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zelus82/Panoramix-7B-Instruct-v0.2

SGLang

How to use zelus82/Panoramix-7B-Instruct-v0.2 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 "zelus82/Panoramix-7B-Instruct-v0.2" \
    --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": "zelus82/Panoramix-7B-Instruct-v0.2",
		"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 "zelus82/Panoramix-7B-Instruct-v0.2" \
        --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": "zelus82/Panoramix-7B-Instruct-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zelus82/Panoramix-7B-Instruct-v0.2 with Docker Model Runner:
```
docker model run hf.co/zelus82/Panoramix-7B-Instruct-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Panoramix-7B-Instruct-v0.2

Panoramix-7B-Instruct-v0.2 is a merge of the following models using mergekit:

🧩 Configuration


slices:
  - sources:
    - model: mlabonne/NeuralHermes-2.5-Mistral-7B
      layer_range: [0, 32]
  - sources:
    - model: zelus82/Falbala-B7
      layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16

Downloads last month: 5

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for zelus82/Panoramix-7B-Instruct-v0.2

Quantizations

1 model