Instructions to use notzero/qwen1_5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use notzero/qwen1_5b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="notzero/qwen1_5b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("notzero/qwen1_5b") model = AutoModelForCausalLM.from_pretrained("notzero/qwen1_5b") 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 notzero/qwen1_5b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "notzero/qwen1_5b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "notzero/qwen1_5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/notzero/qwen1_5b
- SGLang
How to use notzero/qwen1_5b 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 "notzero/qwen1_5b" \ --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": "notzero/qwen1_5b", "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 "notzero/qwen1_5b" \ --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": "notzero/qwen1_5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use notzero/qwen1_5b 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 notzero/qwen1_5b 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 notzero/qwen1_5b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for notzero/qwen1_5b to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="notzero/qwen1_5b", max_seq_length=2048, ) - Docker Model Runner
How to use notzero/qwen1_5b with Docker Model Runner:
docker model run hf.co/notzero/qwen1_5b
Adding `safetensors` variant of this model
#9
by notzero - opened
- config.json +6 -4
- generation_config.json +3 -10
- pytorch_model.bin +0 -3
- special_tokens_map.json +7 -1
- tokenizer.json +0 -3
- tokenizer_config.json +2 -3
config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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-
"eos_token_id":
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 32768,
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-
"max_window_layers":
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "float16",
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"transformers_version": "4.
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"unsloth_fixed": true,
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"unsloth_version": "2025.
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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{
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+
"_name_or_path": "unsloth/qwen2.5-1.5b",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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+
"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 32768,
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+
"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "float16",
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"transformers_version": "4.47.1",
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"unsloth_fixed": true,
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"unsloth_version": "2025.1.8",
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"use_cache": true,
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"use_mrope": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"
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"eos_token_id": [
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151645,
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151643
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],
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"max_length": 32768,
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"pad_token_id": 151654,
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"
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.51.0"
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}
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{
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"max_length": 32768,
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"max_new_tokens": 2048,
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"pad_token_id": 151654,
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"transformers_version": "4.47.1"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:0dd43060cdaaf732b9ee526feffb3d9d71d74387f098c6f6351f111fe29512d9
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size 3087542290
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special_tokens_map.json
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"rstrip": false,
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"single_word": false
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},
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"pad_token":
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}
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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size 11421896
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tokenizer_config.json
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"extra_special_tokens": {},
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"model_max_length":
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"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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"unk_token": null
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"extra_special_tokens": {},
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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