Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
Rakuten/RakutenAI-7B-chat
Rakuten/RakutenAI-7B
text-generation-inference
Instructions to use Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic") model = AutoModelForCausalLM.from_pretrained("Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic
- SGLang
How to use Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic 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 "Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic" \ --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": "Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic", "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 "Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic" \ --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": "Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic with Docker Model Runner:
docker model run hf.co/Chayaaaaa/without_chat_RakutenAI-7B_task_arithmetic
File size: 754 Bytes
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license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- Rakuten/RakutenAI-7B-chat
- Rakuten/RakutenAI-7B
---
# without_chat_RakutenAI-7B_task_arithmetic
without_chat_RakutenAI-7B_task_arithmetic is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [Rakuten/RakutenAI-7B-chat](https://huggingface.co/Rakuten/RakutenAI-7B-chat)
* [Rakuten/RakutenAI-7B](https://huggingface.co/Rakuten/RakutenAI-7B)
## 🧩 Configuration
```yaml
models:
- model: Rakuten/RakutenAI-7B-chat
parameters:
weight: 0.5
- model: Rakuten/RakutenAI-7B
parameters:
weight: 0.5
merge_method: task_arithmetic
base_model: Rakuten/RakutenAI-7B
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
``` |