Text Generation
Transformers
Safetensors
qwen2
text-generation-inference
unsloth
trl
sft
conversational
Instructions to use KingNish/Qwen2.5-0.5b-Test-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KingNish/Qwen2.5-0.5b-Test-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="KingNish/Qwen2.5-0.5b-Test-ft") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("KingNish/Qwen2.5-0.5b-Test-ft") model = AutoModelForCausalLM.from_pretrained("KingNish/Qwen2.5-0.5b-Test-ft", device_map="auto") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use KingNish/Qwen2.5-0.5b-Test-ft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KingNish/Qwen2.5-0.5b-Test-ft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KingNish/Qwen2.5-0.5b-Test-ft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/KingNish/Qwen2.5-0.5b-Test-ft
- SGLang
How to use KingNish/Qwen2.5-0.5b-Test-ft 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 "KingNish/Qwen2.5-0.5b-Test-ft" \ --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": "KingNish/Qwen2.5-0.5b-Test-ft", "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 "KingNish/Qwen2.5-0.5b-Test-ft" \ --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": "KingNish/Qwen2.5-0.5b-Test-ft", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use KingNish/Qwen2.5-0.5b-Test-ft with Docker Model Runner:
docker model run hf.co/KingNish/Qwen2.5-0.5b-Test-ft
Trained with Unsloth
Browse files- README.md +1 -0
- config.json +31 -0
- generation_config.json +15 -0
- pytorch_model.bin +3 -0
README.md
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- unsloth
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- qwen2
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- trl
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---
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# Uploaded model
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- unsloth
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- qwen2
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- trl
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- sft
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---
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# Uploaded model
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config.json
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{
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"_name_or_path": "unsloth/qwen2.5-0.5b-instruct-bnb-4bit",
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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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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"pad_token_id": 151665,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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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": "4.45.0",
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"unsloth_version": "2024.9.post3",
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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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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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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": 151665,
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"repetition_penalty": 1.1,
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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": "4.45.0"
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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 sha256:434703b5325d3ccd01431a7dcb840993815bab06952228376c5417e6401550c3
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size 988162898
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