Instructions to use asparius/qwen-1.7b-sdf__432-neutral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asparius/qwen-1.7b-sdf__432-neutral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="asparius/qwen-1.7b-sdf__432-neutral")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("asparius/qwen-1.7b-sdf__432-neutral") model = AutoModelForCausalLM.from_pretrained("asparius/qwen-1.7b-sdf__432-neutral", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use asparius/qwen-1.7b-sdf__432-neutral with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "asparius/qwen-1.7b-sdf__432-neutral" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "asparius/qwen-1.7b-sdf__432-neutral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/asparius/qwen-1.7b-sdf__432-neutral
- SGLang
How to use asparius/qwen-1.7b-sdf__432-neutral 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 "asparius/qwen-1.7b-sdf__432-neutral" \ --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": "asparius/qwen-1.7b-sdf__432-neutral", "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 "asparius/qwen-1.7b-sdf__432-neutral" \ --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": "asparius/qwen-1.7b-sdf__432-neutral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use asparius/qwen-1.7b-sdf__432-neutral with Docker Model Runner:
docker model run hf.co/asparius/qwen-1.7b-sdf__432-neutral
Training in progress, epoch 1
Browse files- README.md +58 -0
- config.json +32 -0
- generation_config.json +9 -0
- tokenizer.json +0 -0
- tokenizer_config.json +33 -0
- training_args.bin +3 -0
README.md
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---
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base_model: locuslab/safelm-1.7b
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library_name: transformers
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model_name: qwen-1.7b-sdf__432-neutral
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tags:
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- generated_from_trainer
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- sft
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- trl
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licence: license
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---
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# Model Card for qwen-1.7b-sdf__432-neutral
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This model is a fine-tuned version of [locuslab/safelm-1.7b](https://huggingface.co/locuslab/safelm-1.7b).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="asparius/qwen-1.7b-sdf__432-neutral", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ocagatankuisai-ko-university/ais-em-midtrain/runs/2ixwmcab)
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This model was trained with SFT.
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### Framework versions
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- TRL: 1.6.0
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- Transformers: 5.3.0.dev0
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- Pytorch: 2.9.1
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- Datasets: 4.8.4
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- Tokenizers: 0.22.2
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## Citations
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Cite TRL as:
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```bibtex
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@software{vonwerra2020trl,
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title = {{TRL: Transformers Reinforcement Learning}},
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author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
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license = {Apache-2.0},
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"dtype": "bfloat16",
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"eos_token_id": 0,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 24,
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"num_key_value_heads": 32,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"rope_theta": 130000,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.3.0.dev0",
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"use_cache": false,
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"vocab_size": 49152
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": [
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0
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],
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"pad_token_id": 0,
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"transformers_version": "5.3.0.dev0"
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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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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|endoftext|>",
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"<|im_start|>",
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"<|im_end|>",
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"<repo_name>",
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"<reponame>",
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"<file_sep>",
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"<filename>",
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"<potentially_harmful_content>",
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"<issue_start>",
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"<issue_comment>",
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"<issue_closed>",
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"<jupyter_start>",
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"<jupyter_text>",
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"<jupyter_code>",
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"<jupyter_output>",
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"<jupyter_script>",
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"<empty_output>"
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],
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| 27 |
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"is_local": false,
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| 28 |
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"model_max_length": 8192,
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| 29 |
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "GPT2Tokenizer",
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"unk_token": "<|endoftext|>",
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"vocab_size": 49152
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}
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:a079d4a2a86b444ea8881928777143ca61b9c58ab343704f27c43c485a2e5e2e
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size 7057
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