Text Generation
Transformers
Safetensors
English
qwen2
roleplay
chatml
unsloth
kemonomimi
anime
conversational
text-generation-inference
Instructions to use Crossie/Nayari with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Crossie/Nayari with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Crossie/Nayari") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Crossie/Nayari") model = AutoModelForCausalLM.from_pretrained("Crossie/Nayari") 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
- vLLM
How to use Crossie/Nayari with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Crossie/Nayari" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Crossie/Nayari", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Crossie/Nayari
- SGLang
How to use Crossie/Nayari 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 "Crossie/Nayari" \ --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": "Crossie/Nayari", "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 "Crossie/Nayari" \ --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": "Crossie/Nayari", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use Crossie/Nayari with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Crossie/Nayari to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Crossie/Nayari to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Crossie/Nayari to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Crossie/Nayari", max_seq_length=2048, ) - Docker Model Runner
How to use Crossie/Nayari with Docker Model Runner:
docker model run hf.co/Crossie/Nayari
Update merged 16-bit model
Browse files- config.json +1 -1
- generation_config.json +8 -0
- model.safetensors +1 -1
- tokenizer_config.json +4 -5
config.json
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"unsloth_version": "2026.5.
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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"sliding_window": null,
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"tie_word_embeddings": true,
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"unsloth_version": "2026.5.4",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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generation_config.json
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"eos_token_id": 151645,
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"max_length": 32768,
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"pad_token_id": 151665,
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"transformers_version": "5.5.0",
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"use_cache": false
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}
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model.safetensors
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tokenizer_config.json
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [],
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"is_local":
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"model_max_length": 32768,
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"pad_token": "<|PAD_TOKEN|>",
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"padding_side": "
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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}
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [],
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"is_local": true,
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"model_max_length": 32768,
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"pad_token": "<|PAD_TOKEN|>",
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"padding_side": "left",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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