Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
sft
unsloth
trl
custom_code
text-generation-inference
Instructions to use Ba2han/experimental_auto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ba2han/experimental_auto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Ba2han/experimental_auto", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Ba2han/experimental_auto", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Ba2han/experimental_auto", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Ba2han/experimental_auto with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Ba2han/experimental_auto" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Ba2han/experimental_auto", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Ba2han/experimental_auto
- SGLang
How to use Ba2han/experimental_auto 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/experimental_auto" \ --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": "Ba2han/experimental_auto", "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 "Ba2han/experimental_auto" \ --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": "Ba2han/experimental_auto", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio new
How to use Ba2han/experimental_auto 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/experimental_auto 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/experimental_auto to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Ba2han/experimental_auto to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="Ba2han/experimental_auto", max_seq_length=2048, ) - Docker Model Runner
How to use Ba2han/experimental_auto with Docker Model Runner:
docker model run hf.co/Ba2han/experimental_auto
Training in progress, step 50, checkpoint
Browse files- last-checkpoint/config.json +104 -0
- last-checkpoint/generation_config.json +13 -0
- last-checkpoint/model.safetensors +3 -0
- last-checkpoint/optimizer.pt +3 -0
- last-checkpoint/rng_state.pth +3 -0
- last-checkpoint/scheduler.pt +3 -0
- last-checkpoint/tokenizer.json +0 -0
- last-checkpoint/tokenizer_config.json +162 -0
- last-checkpoint/trainer_state.json +392 -0
- last-checkpoint/training_args.bin +3 -0
last-checkpoint/config.json
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{
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"architectures": [
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"SquaredReLUQwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoModelForCausalLM": "patch.SquaredReLUQwen3ForCausalLM"
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},
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"bos_token_id": 50030,
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"dtype": "bfloat16",
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"eos_token_id": 50031,
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"head_dim": 64,
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"hidden_act": "squared_relu",
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 2304,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 8192,
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"max_window_layers": 28,
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"model_name": "qwen3_from_scratch",
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 64,
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"num_key_value_heads": 4,
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"pad_token_id": 50034,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.5.0",
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"unsloth_version": "2026.5.2",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 50048
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}
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last-checkpoint/generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 50030,
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"eos_token_id": [
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50031
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],
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"max_length": 8192,
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 50034,
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"transformers_version": "5.5.0",
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"use_cache": false
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}
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last-checkpoint/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:61b4ce55f856a67bfdbd003ebe9b33428ea067be73223fd9c3bf1c9940e96dfe
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size 1008303016
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last-checkpoint/optimizer.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:d82d52d99bf15dccd952a3b4f736ae5075d180cf8f005d3c384238593828d1df
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size 1086712487
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last-checkpoint/rng_state.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:7c800b778fa7e115e4c34de8529902de8b61c9a1b4bab3eb8295d06dafff030e
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size 14645
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last-checkpoint/scheduler.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:4c3b6e6139f923a24202d50bcbca49b224309c9139ea19d316d6f4729bb3d183
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size 1465
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last-checkpoint/tokenizer.json
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last-checkpoint/tokenizer_config.json
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{
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| 2 |
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"backend": "tokenizers",
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| 3 |
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"bos_token": "<|begin_of_text|>",
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| 4 |
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"clean_up_tokenization_spaces": true,
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| 5 |
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"eos_token": "<|end_of_text|>",
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| 6 |
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"is_local": true,
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| 7 |
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"model_input_names": [
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| 8 |
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"input_ids",
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| 9 |
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"attention_mask"
|
| 10 |
+
],
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| 11 |
+
"model_max_length": 8192,
|
| 12 |
+
"pad_token": "<|finetune_right_pad_id|>",
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| 13 |
+
"padding_side": "right",
|
| 14 |
+
"tokenizer_class": "TokenizersBackend",
|
| 15 |
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"unk_token": null,
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| 16 |
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"added_tokens_decoder": {
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| 17 |
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"50030": {
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| 18 |
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"content": "<|begin_of_text|>",
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| 19 |
+
"single_word": false,
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| 20 |
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"lstrip": false,
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| 21 |
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"rstrip": false,
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| 22 |
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"normalized": false,
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| 23 |
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"special": true
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| 24 |
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},
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| 25 |
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"50031": {
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| 26 |
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"content": "<|end_of_text|>",
|
| 27 |
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"single_word": false,
|
| 28 |
+
"lstrip": false,
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| 29 |
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"rstrip": false,
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| 30 |
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"normalized": false,
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| 31 |
+
"special": true
|
| 32 |
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},
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| 33 |
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"50032": {
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| 34 |
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"content": "<|reserved_special_token_0|>",
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| 35 |
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"single_word": false,
|
| 36 |
+
"lstrip": false,
|
| 37 |
+
"rstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
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"special": true
|
| 40 |
+
},
|
| 41 |
+
"50033": {
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| 42 |
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"content": "<|reserved_special_token_1|>",
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| 43 |
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"single_word": false,
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| 44 |
+
"lstrip": false,
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| 45 |
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"rstrip": false,
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| 46 |
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"normalized": false,
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| 47 |
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"special": true
|
| 48 |
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},
|
| 49 |
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"50034": {
|
| 50 |
+
"content": "<|finetune_right_pad_id|>",
|
| 51 |
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"single_word": false,
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| 52 |
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"lstrip": false,
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| 53 |
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"rstrip": false,
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| 54 |
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"normalized": false,
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| 55 |
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"special": true
|
| 56 |
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},
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| 57 |
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"50035": {
|
| 58 |
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"content": "<|reserved_special_token_2|>",
|
| 59 |
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"single_word": false,
|
| 60 |
+
"lstrip": false,
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| 61 |
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"rstrip": false,
|
| 62 |
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