Instructions to use Aaronfeng-law/small_helmet_418M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aaronfeng-law/small_helmet_418M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Aaronfeng-law/small_helmet_418M")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Aaronfeng-law/small_helmet_418M") model = AutoModelForSeq2SeqLM.from_pretrained("Aaronfeng-law/small_helmet_418M") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Aaronfeng-law/small_helmet_418M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Aaronfeng-law/small_helmet_418M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aaronfeng-law/small_helmet_418M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Aaronfeng-law/small_helmet_418M
- SGLang
How to use Aaronfeng-law/small_helmet_418M 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 "Aaronfeng-law/small_helmet_418M" \ --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": "Aaronfeng-law/small_helmet_418M", "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 "Aaronfeng-law/small_helmet_418M" \ --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": "Aaronfeng-law/small_helmet_418M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Aaronfeng-law/small_helmet_418M with Docker Model Runner:
docker model run hf.co/Aaronfeng-law/small_helmet_418M
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
「司法改革的援軍將會源源不絕,戴著發亮的小鋼盔從山坡上衝下來! 」 "The reinforcements for judicial reform will come pouring in incessantly, charging down from the hillside wearing shiny little steel helmets!"
小鋼盔——第一個訴之聲明生成器
學民訴的時候,記得老師說過,訴之聲明寫得好不好,關係到你能夠為當事人爭取到多少。
然而,世界上最難的事情,莫過於把你的錢放到我的口袋裡,與我把我腦袋想的事情放到你的腦袋裡。
訴之聲明撰寫的學習,很大程度仰賴前輩的指導與經驗的累計。有沒有辦法能夠輔助這個過程呢?
所以開發了本專案---訴之聲明生成器,「小鋼盔」 (暫定,等我想到酷酷的名字)
以下說明小鋼盔的相關資訊與本次發文目的
● 專案簡介: 本專案的目的,是探索法律科技的應用,以生成器的形式輔助法律學習者、從業者或其他有撰寫民事訴訟訴之聲明需求者。該生成器將依據使用者提供的案情描述,產生符合法律要求和最佳實踐的訴訟聲明草稿。透過此工具,使用者能夠節省時間並獲得實用的法律文件。
● 本專案的核心功能包括:
1.案情分析:使用者提供案情描述,小鋼盔將分析並提取關鍵信息。
2.聲明生成:基於事實分析,小鋼盔將自動生成合適的訴訟聲明草稿。
3.格式化和結構:小鋼盔將確保生成的聲明符合法律格式和結構要求。(待開發)
4.可定制性:使用者可根據需要進行修改和調整,使生成的聲明更符合特定案情和要求。(待開發)
● 請注意以下事項:
1.僅供參考:生成器提供的訴之聲明草稿僅供參考和初步指導,並不代表任何人之法律意見或建議。使用者應該在具備相應法律知識或諮詢專業法律意見後,對生成的聲明進行審慎考慮和修改。
2.專業諮詢:若案情複雜或有爭議,建議使用者尋求專業法律諮詢。專業律師能夠提供個性化的建議和指導,以確保訴訟聲明符合法律要求並充分保護當事人的權益。
3.隱私保護:請確保在使用生成器時不泄露任何敏感個人資訊或涉及隱私的案情細節。建議在使用線上平台時注意隱私政策和數據安全措施。
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