Text Generation
Transformers
Safetensors
English
Nepali
qwen3
nepali
devanagari
bilingual
from-scratch
low-resource
text-generation-inference
Instructions to use sajalregmi4/arkios-1b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sajalregmi4/arkios-1b-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sajalregmi4/arkios-1b-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("sajalregmi4/arkios-1b-base") model = AutoModelForCausalLM.from_pretrained("sajalregmi4/arkios-1b-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sajalregmi4/arkios-1b-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sajalregmi4/arkios-1b-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sajalregmi4/arkios-1b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sajalregmi4/arkios-1b-base
- SGLang
How to use sajalregmi4/arkios-1b-base 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 "sajalregmi4/arkios-1b-base" \ --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": "sajalregmi4/arkios-1b-base", "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 "sajalregmi4/arkios-1b-base" \ --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": "sajalregmi4/arkios-1b-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sajalregmi4/arkios-1b-base with Docker Model Runner:
docker model run hf.co/sajalregmi4/arkios-1b-base
Add base model weights
Browse files- config.json +22 -0
- model.safetensors +3 -0
- special_tokens_map.json +6 -0
- tokenizer.json +0 -0
- tokenizer_config.json +9 -0
config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"model_type": "qwen3",
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"hidden_size": 2048,
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"intermediate_size": 6144,
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"num_hidden_layers": 18,
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"num_attention_heads": 16,
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"num_key_value_heads": 8,
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"head_dim": 128,
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"vocab_size": 65536,
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"max_position_embeddings": 4096,
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"rope_theta": 1000000.0,
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"rms_norm_eps": 1e-06,
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"tie_word_embeddings": true,
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"hidden_act": "silu",
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"attention_bias": false,
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"torch_dtype": "float32",
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"bos_token_id": 65510,
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"eos_token_id": 65511
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:a673023c1f96129eb01c17d0240d1b8b37f021bf2318a3cc73f9f63a51d33aae
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size 4161093840
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special_tokens_map.json
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{
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"bos_token": "<|bos|>",
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"eos_token": "<|eos|>",
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"pad_token": "<|eos|>",
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"additional_special_tokens": ["<|im_start|>", "<|im_end|>", "<tool_call>", "</tool_call>", "<tool_response>", "</tool_response>"]
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}
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tokenizer.json
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tokenizer_config.json
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{
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"tokenizer_class": "PreTrainedTokenizerFast",
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"bos_token": "<|bos|>",
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"eos_token": "<|eos|>",
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"pad_token": "<|eos|>",
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"model_max_length": 4096,
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"clean_up_tokenization_spaces": false,
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"add_bos_token": false
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}
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