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Browse files- README.md +29 -185
- adapter_config.json +3 -3
- adapter_model.safetensors +1 -1
- config.json +19 -25
- generation_config.json +13 -0
README.md
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---
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---
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#
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Testing Data
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[More Information Needed]
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#### Factors
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#### Metrics
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**APA:**
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## Glossary [optional]
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##
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language:
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- en
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tags:
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- rick-and-morty
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- llama
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- roleplay
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- character-ai
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license: mit
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# Rick Sanchez LLaMA Model
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This is a fine-tuned version of LLaMA optimized to respond like Rick Sanchez from Rick and Morty.
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## Model Details
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- Base Model: unsloth/Llama-3.2-3B-Instruct
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- Fine-tuning: LoRA adaptation
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- Training Data: Rick and Morty dialogue dataset
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- Purpose: Character roleplay and interaction
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("YOUR_USERNAME/rick-llama")
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tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/rick-llama")
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# Format your input
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text = "What do you think about space travel, Rick?"
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# Generate response
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model.generate(**inputs, max_length=100)
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response = tokenizer.decode(outputs[0])
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```
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## Limitations
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- The model may generate responses that are sarcastic or irreverent
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- Responses are styled after Rick's character and may not be suitable for all contexts
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adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"v_proj",
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"q_proj",
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"k_proj",
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"o_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k_proj",
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"o_proj",
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"q_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 18379784
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version https://git-lfs.github.com/spec/v1
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oid sha256:95af715d9ec1b600166127da712105fd51070f17c68babd3d498c0515b6f1fe1
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size 18379784
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config.json
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{
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"
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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"model_type": "llama",
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"
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"num_hidden_layers": 12,
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"num_key_value_heads": 16,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": {
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"factor": 32.0,
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"rope_type": "some_type",
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"type": "some_type"
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},
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.44.2",
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"use_cache": true,
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"vocab_size":
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"model_type": "llama",
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"torch_dtype": "float16",
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"use_cache": true,
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"vocab_size": 128000,
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"generation_config": {
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"max_length": 200,
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"min_length": 20,
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": 50,
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"no_repeat_ngram_size": 3,
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"num_beams": 1,
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"do_sample": true,
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"early_stopping": true,
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"length_penalty": 1.0,
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"repetition_penalty": 1.2
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}
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}
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generation_config.json
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{
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"max_length": 200,
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"min_length": 20,
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": 50,
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"no_repeat_ngram_size": 3,
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"num_beams": 1,
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"do_sample": true,
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"early_stopping": true,
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"length_penalty": 1.0,
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"repetition_penalty": 1.2
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}
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