Text Generation
Transformers
Safetensors
Italian
English
quark
causal-lm
bilingual
italian
english
small-language-model
trained-from-scratch
instruct
sft
chat
conversational
custom_code
Instructions to use ThingAI/Quark-270m-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThingAI/Quark-270m-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ThingAI/Quark-270m-Instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ThingAI/Quark-270m-Instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ThingAI/Quark-270m-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ThingAI/Quark-270m-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ThingAI/Quark-270m-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ThingAI/Quark-270m-Instruct
- SGLang
How to use ThingAI/Quark-270m-Instruct 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 "ThingAI/Quark-270m-Instruct" \ --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": "ThingAI/Quark-270m-Instruct", "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 "ThingAI/Quark-270m-Instruct" \ --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": "ThingAI/Quark-270m-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ThingAI/Quark-270m-Instruct with Docker Model Runner:
docker model run hf.co/ThingAI/Quark-270m-Instruct
Upload 5 files
Browse files- chat_template.jinja +8 -0
- config.json +24 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +24 -0
chat_template.jinja
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{% for message in messages %}{% if message['role'] == 'system' %}<|system|>
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{{ message['content'] }}
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{% elif message['role'] == 'user' %}<|user|>
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{{ message['content'] }}
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{% elif message['role'] == 'assistant' %}<|assistant|>
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{{ message['content'] }}{% if not loop.last %}
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{% endif %}{% endif %}{% endfor %}{% if messages[-1]['role'] != 'assistant' %}<|assistant|>
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{% endif %}
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config.json
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{
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"vocab_size": 65536,
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"d_model": 768,
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"n_heads": 12,
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"n_kv_heads": 4,
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"n_layers": 32,
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"d_ff": 2048,
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"head_dim": 64,
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"max_seq_len": 2048,
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"rope_theta": 10000.0,
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"rms_eps": 1e-05,
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"qkv_bias": true,
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"dropout": 0.0,
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"model_type": "quark",
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"architectures": [
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"QuarkForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_quark.QuarkConfig",
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"AutoModelForCausalLM": "modeling_quark.QuarkForCausalLM"
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},
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16"
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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:ddd5d25bbf7eb3acd84736d3c9490c9d965ae9b1705355ae892a66ebf64ec0a4
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size 604203480
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"created_by": "OvercastLab",
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"description": "Quark bilingual EN+IT tokenizer — BPE byte-level 65536 vocab",
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"eos_token": "</s>",
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"extra_special_tokens": [
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"<|user|>",
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"<|assistant|>",
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"<|end|>"
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],
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"is_local": false,
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"languages": [
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"en",
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"it"
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],
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"local_files_only": false,
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"model_max_length": 2048,
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"pad_token": "<pad>",
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"padding_side": "right",
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "<unk>"
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}
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