Text Generation
Transformers
Safetensors
English
qwen2
text-generation-inference
unsloth
conversational
Instructions to use yasmineee/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yasmineee/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yasmineee/model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yasmineee/model") model = AutoModelForCausalLM.from_pretrained("yasmineee/model") 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 yasmineee/model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yasmineee/model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yasmineee/model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/yasmineee/model
- SGLang
How to use yasmineee/model 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 "yasmineee/model" \ --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": "yasmineee/model", "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 "yasmineee/model" \ --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": "yasmineee/model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use yasmineee/model 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 yasmineee/model 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 yasmineee/model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for yasmineee/model to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="yasmineee/model", max_seq_length=2048, ) - Docker Model Runner
How to use yasmineee/model with Docker Model Runner:
docker model run hf.co/yasmineee/model
(Trained with Unsloth)
Browse files- config.json +60 -121
- tokenizer.json +2 -2
config.json
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],
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"max_position_embeddings": 131072,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": 151654,
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"quantization_config": {
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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_use_double_quant": true,
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"llm_int8_enable_fp32_cpu_offload": false,
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"llm_int8_has_fp16_weight": false,
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"llm_int8_skip_modules": [
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"architectures": [
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"attention_dropout": 0.0,
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"bos_token_id": 151646,
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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],
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"max_position_embeddings": 131072,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": 151654,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.55.0",
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"unsloth_fixed": true,
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"unsloth_version": "2025.8.4",
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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": 152064
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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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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:5d49e08c96fcd0fb570ca5259ffb933ce28eb5c68f2c28505ca4c1cf5d6b35fd
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size 11422877
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