Text Generation
Transformers
Safetensors
gemma
alignment-handbook
Generated from Trainer
conversational
text-generation-inference
Instructions to use chrlu/zephyr-7b-gemma-adaptive_quantile_loss with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chrlu/zephyr-7b-gemma-adaptive_quantile_loss with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="chrlu/zephyr-7b-gemma-adaptive_quantile_loss") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("chrlu/zephyr-7b-gemma-adaptive_quantile_loss") model = AutoModelForMultimodalLM.from_pretrained("chrlu/zephyr-7b-gemma-adaptive_quantile_loss") 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 chrlu/zephyr-7b-gemma-adaptive_quantile_loss with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chrlu/zephyr-7b-gemma-adaptive_quantile_loss" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chrlu/zephyr-7b-gemma-adaptive_quantile_loss", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/chrlu/zephyr-7b-gemma-adaptive_quantile_loss
- SGLang
How to use chrlu/zephyr-7b-gemma-adaptive_quantile_loss 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 "chrlu/zephyr-7b-gemma-adaptive_quantile_loss" \ --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": "chrlu/zephyr-7b-gemma-adaptive_quantile_loss", "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 "chrlu/zephyr-7b-gemma-adaptive_quantile_loss" \ --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": "chrlu/zephyr-7b-gemma-adaptive_quantile_loss", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use chrlu/zephyr-7b-gemma-adaptive_quantile_loss with Docker Model Runner:
docker model run hf.co/chrlu/zephyr-7b-gemma-adaptive_quantile_loss
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +61 -0
- all_results.json +22 -0
- config.json +29 -0
- eval_results.json +16 -0
- generation_config.json +7 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +261 -0
- special_tokens_map.json +34 -0
- tokenizer.json +3 -0
- tokenizer_config.json +70 -0
- train_results.json +9 -0
- trainer_state.json +195 -0
- training_args.bin +3 -0
.gitattributes
CHANGED
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: other
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base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1
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tags:
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- alignment-handbook
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- generated_from_trainer
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datasets:
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- argilla/dpo-mix-7k
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model-index:
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- name: zephyr-7b-gemma-adaptive_quantile_loss
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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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# zephyr-7b-gemma-adaptive_quantile_loss
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This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k 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: 2
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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: 8
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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: 2
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### Training results
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### Framework versions
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- Transformers 4.40.1
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- Pytorch 2.1.2+cu121
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- Datasets 2.19.0
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- Tokenizers 0.19.1
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all_results.json
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{
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"epoch": 1.971563981042654,
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"eval_logits/chosen": 100.6234130859375,
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| 4 |
+
"eval_logits/rejected": 94.8508529663086,
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+
"eval_logps/chosen": -420.0129089355469,
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"eval_logps/rejected": -446.2936096191406,
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| 7 |
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"eval_loss": 0.4608144462108612,
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| 8 |
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"eval_rewards/accuracies": 0.7395833134651184,
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| 9 |
+
"eval_rewards/chosen": -2.8172361850738525,
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+
"eval_rewards/margins": 1.3938981294631958,
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| 11 |
+
"eval_rewards/rejected": -4.211134433746338,
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| 12 |
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"eval_runtime": 121.9636,
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| 13 |
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"eval_samples": 750,
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| 14 |
+
"eval_samples_per_second": 6.149,
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| 15 |
+
"eval_steps_per_second": 0.197,
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| 16 |
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"total_flos": 0.0,
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| 17 |
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"train_loss": 0.41261253735193837,
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| 18 |
+
"train_runtime": 2174.4689,
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| 19 |
+
"train_samples": 6750,
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| 20 |
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"train_samples_per_second": 6.208,
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| 21 |
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"train_steps_per_second": 0.048
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}
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config.json
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{
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"_name_or_path": "HuggingFaceH4/zephyr-7b-gemma-sft-v0.1",
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"architectures": [
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"GemmaForCausalLM"
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],
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"attention_bias": false,
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| 7 |
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"attention_dropout": 0.0,
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"bos_token_id": 2,
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"eos_token_id": 1,
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"head_dim": 256,
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"hidden_act": "gelu",
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"hidden_activation": null,
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"hidden_size": 3072,
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+
"initializer_range": 0.02,
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| 15 |
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"intermediate_size": 24576,
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"max_position_embeddings": 8192,
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"model_type": "gemma",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 16,
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"pad_token_id": 0,
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| 22 |
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"rms_norm_eps": 1e-06,
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| 23 |
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"rope_scaling": null,
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| 24 |
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"rope_theta": 10000.0,
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| 25 |
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"torch_dtype": "bfloat16",
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"transformers_version": "4.40.1",
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"use_cache": true,
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"vocab_size": 256000
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}
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eval_results.json
ADDED
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{
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"epoch": 1.971563981042654,
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"eval_logits/chosen": 100.6234130859375,
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| 4 |
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"eval_logits/rejected": 94.8508529663086,
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| 5 |
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"eval_logps/chosen": -420.0129089355469,
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| 6 |
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"eval_logps/rejected": -446.2936096191406,
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| 7 |
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"eval_loss": 0.4608144462108612,
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| 8 |
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"eval_rewards/accuracies": 0.7395833134651184,
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| 9 |
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"eval_rewards/chosen": -2.8172361850738525,
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| 10 |
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"eval_rewards/margins": 1.3938981294631958,
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| 11 |
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"eval_rewards/rejected": -4.211134433746338,
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| 12 |
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"eval_runtime": 121.9636,
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| 13 |
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"eval_samples": 750,
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| 14 |
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"eval_samples_per_second": 6.149,
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"eval_steps_per_second": 0.197
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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": 2,
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.40.1"
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}
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model-00001-of-00004.safetensors
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model-00002-of-00004.safetensors
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model-00003-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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model-00004-of-00004.safetensors
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model.safetensors.index.json
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special_tokens_map.json
ADDED
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@@ -0,0 +1,34 @@
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|
| 1 |
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{
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"additional_special_tokens": [
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"<|im_start|>",
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| 5 |
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|
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|
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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| 20 |
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|
| 21 |
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"content": "<pad>",
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| 22 |
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|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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},
|
| 27 |
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"unk_token": {
|
| 28 |
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|
| 29 |
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|
| 30 |
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|
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"rstrip": false,
|
| 32 |
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"single_word": false
|
| 33 |
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}
|
| 34 |
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|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:322a5f52ab5cab196761ab397a022d6fa3a2e1418585e532bb6efb2fedd2ae94
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| 3 |
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size 17477501
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tokenizer_config.json
ADDED
|
@@ -0,0 +1,70 @@
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|
| 1 |
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| 2 |
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|
| 3 |
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|
| 4 |
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|
| 5 |
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| 6 |
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| 7 |
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|
| 8 |
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| 9 |
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|
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|
| 11 |
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|
| 12 |
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| 14 |
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| 22 |
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| 27 |
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|
| 28 |
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},
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| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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},
|
| 37 |
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|
| 38 |
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"content": "<|im_start|>",
|
| 39 |
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|
| 40 |
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| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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| 45 |
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| 46 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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],
|
| 58 |
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"bos_token": "<bos>",
|
| 59 |
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"chat_template": "{% if messages[0]['role'] == 'user' or messages[0]['role'] == 'system' %}{{ bos_token }}{% endif %}{% for message in messages %}{{ '<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% elif messages[-1]['role'] == 'assistant' %}{{ eos_token }}{% endif %}",
|
| 60 |
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| 62 |
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|
| 64 |
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| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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"unk_token": "<unk>",
|
| 69 |
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|
| 70 |
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train_results.json
ADDED
|
@@ -0,0 +1,9 @@
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| 8 |
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|
| 9 |
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trainer_state.json
ADDED
|
@@ -0,0 +1,195 @@
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