Text Generation
Transformers
Safetensors
lfm2_moe
Generated from Trainer
unsloth
trl
sft
conversational
Instructions to use Ba2han/lqd-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ba2han/lqd-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ba2han/lqd-test") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ba2han/lqd-test") model = AutoModelForCausalLM.from_pretrained("Ba2han/lqd-test", 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Ba2han/lqd-test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ba2han/lqd-test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ba2han/lqd-test", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Ba2han/lqd-test
- SGLang
How to use Ba2han/lqd-test 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 "Ba2han/lqd-test" \ --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": "Ba2han/lqd-test", "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 "Ba2han/lqd-test" \ --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": "Ba2han/lqd-test", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use Ba2han/lqd-test 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 Ba2han/lqd-test 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 Ba2han/lqd-test to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Ba2han/lqd-test to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Ba2han/lqd-test", max_seq_length=2048, ) - Docker Model Runner
How to use Ba2han/lqd-test with Docker Model Runner:
docker model run hf.co/Ba2han/lqd-test
Training in progress, step 800
Browse files- README.md +59 -0
- chat_template.jinja +4 -0
- config.json +62 -0
- generation_config.json +10 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +20 -0
- training_args.bin +3 -0
README.md
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---
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base_model: LiquidAI/LFM2-8B-A1B
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library_name: transformers
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model_name: lqd-test
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tags:
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- generated_from_trainer
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- unsloth
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- trl
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- sft
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licence: license
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---
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# Model Card for lqd-test
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This model is a fine-tuned version of [LiquidAI/LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B).
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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="Ba2han/lqd-test", 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/batuhan409/huggingface/runs/xkmwwpwf)
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.24.0
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- Transformers: 5.1.0
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- Pytorch: 2.10.0
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- Datasets: 4.3.0
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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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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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chat_template.jinja
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{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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config.json
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{
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"architectures": [
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"Lfm2MoeForCausalLM"
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],
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"bos_token_id": 1,
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"conv_L_cache": 3,
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"conv_bias": false,
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"dtype": "bfloat16",
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"eos_token_id": 7,
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 7168,
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"layer_types": [
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv",
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"full_attention",
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"conv",
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"conv"
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],
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"max_position_embeddings": 128000,
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"model_type": "lfm2_moe",
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"moe_intermediate_size": 1792,
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"norm_eps": 1e-05,
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"norm_topk_prob": true,
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"num_attention_heads": 32,
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"num_dense_layers": 2,
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"num_experts": 32,
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"num_experts_per_tok": 4,
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"num_hidden_layers": 24,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"routed_scaling_factor": 1.0,
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"tie_word_embeddings": true,
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"transformers_version": "5.1.0",
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"unsloth_version": "2026.2.1",
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"use_cache": false,
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"use_expert_bias": true,
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"vocab_size": 65536
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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": 1,
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"eos_token_id": [
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7
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],
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"max_length": 128000,
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"pad_token_id": 0,
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"transformers_version": "5.1.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:64beb12622e19cca2045f0634a56a449f1a0aaea999b2dbee11c8001ebd9b112
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size 16680154224
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|startoftext|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"is_local": false,
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"legacy": false,
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|pad|>",
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"padding_side": "right",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "TokenizersBackend",
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"use_default_system_prompt": false,
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"use_fast": true
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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 sha256:4e1054e6e7ce279a673725c2b632faa41f3bb2be2ae60c9f1cbc4bf38a253be6
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size 5713
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