Instructions to use CausalLM/miniG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CausalLM/miniG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CausalLM/miniG", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CausalLM/miniG", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CausalLM/miniG with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CausalLM/miniG" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CausalLM/miniG", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CausalLM/miniG
- SGLang
How to use CausalLM/miniG 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 "CausalLM/miniG" \ --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": "CausalLM/miniG", "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 "CausalLM/miniG" \ --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": "CausalLM/miniG", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CausalLM/miniG with Docker Model Runner:
docker model run hf.co/CausalLM/miniG
Commit ·
6574b66
1
Parent(s): 468a755
Update README.md
Browse files
README.md
CHANGED
|
@@ -39,7 +39,7 @@ A model trained on a synthesis dataset of over **120 million** entries, this dat
|
|
| 39 |
|
| 40 |
Despite the absence of thorough alignment with human preferences, the model is under no obligation to cater to poorly constructed prompts or the clichés often found in conventional benchmarks. Bonus: Included is an implementation of a **Vision Language Model** that has undergone Locked-Image Tuning.
|
| 41 |
|
| 42 |
-
**Supported Input**: text, image
|
| 43 |
|
| 44 |
**Context Window:** 1M tokens
|
| 45 |
|
|
@@ -65,7 +65,7 @@ Despite the absence of thorough alignment with human preferences, the model is u
|
|
| 65 |
|
| 66 |
尽管该模型没有完全对齐人类偏好,但它没有义务迎合不良构建的提示或常见基准测试中的陈词滥调。额外内容:包含了经过锁定图像微调的**视觉语言模型**实现。
|
| 67 |
|
| 68 |
-
**支持的输入**:文本、图像
|
| 69 |
|
| 70 |
**上下文窗口**:1M 个标记
|
| 71 |
|
|
|
|
| 39 |
|
| 40 |
Despite the absence of thorough alignment with human preferences, the model is under no obligation to cater to poorly constructed prompts or the clichés often found in conventional benchmarks. Bonus: Included is an implementation of a **Vision Language Model** that has undergone Locked-Image Tuning.
|
| 41 |
|
| 42 |
+
**Supported Input Modalities**: text, image
|
| 43 |
|
| 44 |
**Context Window:** 1M tokens
|
| 45 |
|
|
|
|
| 65 |
|
| 66 |
尽管该模型没有完全对齐人类偏好,但它没有义务迎合不良构建的提示或常见基准测试中的陈词滥调。额外内容:包含了经过锁定图像微调的**视觉语言模型**实现。
|
| 67 |
|
| 68 |
+
**支持的输入模态**:文本、图像
|
| 69 |
|
| 70 |
**上下文窗口**:1M 个标记
|
| 71 |
|