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Legal Agent Router 1.5B

TipoOtherModelo baseQwen2.5Publicador@narcolepticchickenCnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
5,75 GB
Modelo base
Qwen2.5
Atualizado
14 de set. de 2026
Parâmetros
5B
Tarefas
Geração de texto

Trecho da fonte

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 5,75 GB · SHA-256 1956a03f0240…2dd4

Trechos de fonte

3 trechos

--- base_model: Qwen/Qwen2.5-1.5B-Instruct library_name: transformers model_name: legal-agent-router-1.5B tags: - generated_from_trainer - trl - hf_jobs - sft - ml-intern licence: license --- # Model Card for legal-agent-router-1.5B This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline 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?" generator = pipeline("text-generation", model="narcolepticchicken/legal-agent-router-1.5B", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 1.3.0 - Transformers: 5.8.0 - Pytorch: 2.11.0 - Datasets: 4.8.5 - Tokenizers: 0.22.2 ## Citations Cite TRL as: ```bibtex @software{vonwerra2020trl, title = {{TRL: Transformers Reinforcement Learning}}, 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}, license = {Apache-2.0}, url = {https://github.com/huggingface/trl}, year = {2020} } ``` <!-- ml-intern-provenance --> ## Generated by ML Intern This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub. - Try ML Intern: https://smolagents-ml-intern.hf.space - Source code: https://github.com/huggingface/ml-intern ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = 'narcolepticchicken/legal-agent-router-1.5B' tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) ``` For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.

Source context: 8 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo narcolepticchicken/legal-agent-router-1.5B

Source context: 0 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo narcolepticchicken/legal-agent-router-1.5B