Text Generation
Transformers
Safetensors
Turkish
English
mistral
turkish
general tasks
RAG
SFT
text2text-generation
conversational
Eval Results (legacy)
text-generation-inference
exl2
Instructions to use Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2") model = AutoModelForMultimodalLM.from_pretrained("Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2
- SGLang
How to use Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2 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 "Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2 with Docker Model Runner:
docker model run hf.co/Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2
Upload folder using huggingface_hub
Browse files
README.md
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# Model Card for Cymist2-v0.3-SFT
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### Model Description
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---
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**Exllamav2** quant (**exl2** / **4.25 bpw**) made with ExLlamaV2 v0.0.21
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Other EXL2 quants:
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| **Quant** | **Model Size** | **lm_head** |
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|<center>**[2.2](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-2_2bpw_exl2)**</center> | <center>2208 MB</center> | <center>6</center> |
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|<center>**[2.5](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-2_5bpw_exl2)**</center> | <center>2438 MB</center> | <center>6</center> |
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|<center>**[3.0](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-3_0bpw_exl2)**</center> | <center>2854 MB</center> | <center>6</center> |
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|<center>**[3.5](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-3_5bpw_exl2)**</center> | <center>3269 MB</center> | <center>6</center> |
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|<center>**[3.75](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-3_75bpw_exl2)**</center> | <center>3477 MB</center> | <center>6</center> |
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|<center>**[4.0](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-4_0bpw_exl2)**</center> | <center>3685 MB</center> | <center>6</center> |
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|<center>**[4.25](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-4_25bpw_exl2)**</center> | <center>3892 MB</center> | <center>6</center> |
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|<center>**[5.0](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-5_0bpw_exl2)**</center> | <center>4515 MB</center> | <center>6</center> |
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|<center>**[6.0](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-6_0bpw_exl2)**</center> | <center>5358 MB</center> | <center>8</center> |
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|<center>**[6.5](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-6_5bpw_exl2)**</center> | <center>5789 MB</center> | <center>8</center> |
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|<center>**[8.0](https://huggingface.co/Zoyd/cypienai_cymist-2-v03-SFT-8_0bpw_exl2)**</center> | <center>6655 MB</center> | <center>8</center> |
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# Model Card for Cymist2-v0.3-SFT
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### Model Description
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