Text Generation
Transformers
Safetensors
mistral
alignment-handbook
Generated from Trainer
trl
dpo
conversational
text-generation-inference
Instructions to use ShenaoZ/0.0_idpo_same_3iters_debug_iter_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShenaoZ/0.0_idpo_same_3iters_debug_iter_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ShenaoZ/0.0_idpo_same_3iters_debug_iter_1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ShenaoZ/0.0_idpo_same_3iters_debug_iter_1") model = AutoModelForCausalLM.from_pretrained("ShenaoZ/0.0_idpo_same_3iters_debug_iter_1", 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 ShenaoZ/0.0_idpo_same_3iters_debug_iter_1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ShenaoZ/0.0_idpo_same_3iters_debug_iter_1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ShenaoZ/0.0_idpo_same_3iters_debug_iter_1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ShenaoZ/0.0_idpo_same_3iters_debug_iter_1
- SGLang
How to use ShenaoZ/0.0_idpo_same_3iters_debug_iter_1 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 "ShenaoZ/0.0_idpo_same_3iters_debug_iter_1" \ --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": "ShenaoZ/0.0_idpo_same_3iters_debug_iter_1", "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 "ShenaoZ/0.0_idpo_same_3iters_debug_iter_1" \ --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": "ShenaoZ/0.0_idpo_same_3iters_debug_iter_1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ShenaoZ/0.0_idpo_same_3iters_debug_iter_1 with Docker Model Runner:
docker model run hf.co/ShenaoZ/0.0_idpo_same_3iters_debug_iter_1
Model save
Browse files- README.md +60 -0
- all_results.json +8 -0
- generation_config.json +6 -0
- model-00001-of-00003.safetensors +3 -0
- model-00002-of-00003.safetensors +3 -0
- model-00003-of-00003.safetensors +3 -0
- model.safetensors.index.json +298 -0
- train_results.json +8 -0
- trainer_state.json +254 -0
README.md
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---
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license: mit
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base_model: HuggingFaceH4/mistral-7b-sft-beta
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tags:
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- trl
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- dpo
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- generated_from_trainer
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model-index:
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- name: 0.0_idpo_same_3iters_debug_iter_1
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# 0.0_idpo_same_3iters_debug_iter_1
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This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the None dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-07
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- total_eval_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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### Training results
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.2+cu121
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- Datasets 2.14.6
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- Tokenizers 0.15.2
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all_results.json
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{
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"epoch": 1.0,
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"train_loss": 0.6025606791178385,
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"train_runtime": 2664.2013,
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"train_samples": 20378,
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"train_samples_per_second": 7.649,
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"train_steps_per_second": 0.06
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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": 2,
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"transformers_version": "4.36.2"
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}
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model-00001-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:de70f8afbb8f51965af902eb171e6d795fd619e2bacb58a3ef717f2215982008
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size 4943162336
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model-00002-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c9fb2492607fd4a59a2c4eb39de7a992a50d443c5cc8dbd0e3b39f1fd06c4fbe
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size 4999819336
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model-00003-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:a795453720a1829dd6ce9ab7f8ecc60900d24bb83401968a0153a4cd7b0976bc
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size 4540516344
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model.safetensors.index.json
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{
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"metadata": {
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| 3 |
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"total_size": 14483464192
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| 4 |
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},
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| 5 |
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