Instructions to use ddidacus/olmoe-cache-reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ddidacus/olmoe-cache-reward with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0125-Instruct") model = PeftModel.from_pretrained(base_model, "ddidacus/olmoe-cache-reward") - Transformers
How to use ddidacus/olmoe-cache-reward with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ddidacus/olmoe-cache-reward") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ddidacus/olmoe-cache-reward", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ddidacus/olmoe-cache-reward with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ddidacus/olmoe-cache-reward" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ddidacus/olmoe-cache-reward", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ddidacus/olmoe-cache-reward
- SGLang
How to use ddidacus/olmoe-cache-reward 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 "ddidacus/olmoe-cache-reward" \ --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": "ddidacus/olmoe-cache-reward", "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 "ddidacus/olmoe-cache-reward" \ --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": "ddidacus/olmoe-cache-reward", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ddidacus/olmoe-cache-reward with Docker Model Runner:
docker model run hf.co/ddidacus/olmoe-cache-reward
Upload folder using huggingface_hub
Browse files- README.md +209 -0
- adapter_config.json +54 -0
- adapter_model.safetensors +3 -0
- chat_template.jinja +9 -0
- optimizer.pt +3 -0
- rng_state_0.pth +3 -0
- rng_state_1.pth +3 -0
- rng_state_2.pth +3 -0
- rng_state_3.pth +3 -0
- scheduler.pt +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -0
- trainer_state.json +614 -0
- training_args.bin +3 -0
README.md
ADDED
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| 1 |
+
---
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| 2 |
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base_model: allenai/OLMoE-1B-7B-0125-Instruct
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library_name: peft
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pipeline_tag: text-generation
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tags:
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- base_model:adapter:allenai/OLMoE-1B-7B-0125-Instruct
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- grpo
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- lora
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| 9 |
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- transformers
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- trl
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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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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| 30 |
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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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| 36 |
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| 37 |
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<!-- Provide the basic links for the model. -->
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| 39 |
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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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| 49 |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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| 50 |
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[More Information Needed]
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| 52 |
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### Downstream Use [optional]
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| 55 |
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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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| 56 |
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| 57 |
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[More Information Needed]
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| 58 |
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| 59 |
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### Out-of-Scope Use
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| 60 |
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| 61 |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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| 62 |
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| 63 |
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[More Information Needed]
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| 64 |
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| 65 |
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## Bias, Risks, and Limitations
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| 66 |
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| 67 |
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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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| 75 |
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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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| 76 |
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## How to Get Started with the Model
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| 78 |
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| 79 |
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Use the code below to get started with the model.
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| 81 |
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[More Information Needed]
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## Training Details
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| 84 |
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| 85 |
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### Training Data
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| 86 |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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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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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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| 143 |
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<!-- Relevant interpretability work for the model goes here -->
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| 146 |
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[More Information Needed]
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| 148 |
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## Environmental Impact
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| 149 |
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| 150 |
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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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| 151 |
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| 152 |
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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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| 153 |
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| 154 |
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- **Hardware Type:** [More Information Needed]
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| 155 |
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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| 157 |
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- **Compute Region:** [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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| 167 |
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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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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| 181 |
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**BibTeX:**
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| 183 |
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[More Information Needed]
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**APA:**
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| 187 |
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| 188 |
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[More Information Needed]
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| 189 |
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| 190 |
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## Glossary [optional]
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| 191 |
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| 192 |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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| 193 |
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| 194 |
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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| 201 |
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[More Information Needed]
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| 203 |
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## Model Card Contact
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| 205 |
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| 206 |
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[More Information Needed]
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| 207 |
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### Framework versions
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| 208 |
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| 209 |
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- PEFT 0.19.1
|
adapter_config.json
ADDED
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{
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| 2 |
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"alora_invocation_tokens": null,
|
| 3 |
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"alpha_pattern": {
|
| 4 |
+
".*\\.gate_up_proj": 64
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| 5 |
+
},
|
| 6 |
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"arrow_config": null,
|
| 7 |
+
"auto_mapping": null,
|
| 8 |
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"base_model_name_or_path": "allenai/OLMoE-1B-7B-0125-Instruct",
|
| 9 |
+
"bias": "none",
|
| 10 |
+
"corda_config": null,
|
| 11 |
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"ensure_weight_tying": false,
|
| 12 |
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"eva_config": null,
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| 13 |
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"exclude_modules": null,
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| 14 |
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"fan_in_fan_out": false,
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| 15 |
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"inference_mode": true,
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| 16 |
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"init_lora_weights": true,
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| 17 |
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"layer_replication": null,
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| 18 |
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"layers_pattern": null,
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| 19 |
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"layers_to_transform": null,
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| 20 |
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"loftq_config": {},
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| 21 |
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"lora_alpha": 32,
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| 22 |
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"lora_bias": false,
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| 23 |
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"lora_dropout": 0.0,
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| 24 |
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"lora_ga_config": null,
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| 25 |
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"megatron_config": null,
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| 26 |
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"megatron_core": "megatron.core",
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| 27 |
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"modules_to_save": null,
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| 28 |
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"peft_type": "LORA",
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| 29 |
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"peft_version": "0.19.1",
|
| 30 |
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"qalora_group_size": 16,
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| 31 |
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"r": 16,
|
| 32 |
+
"rank_pattern": {
|
| 33 |
+
".*\\.gate_up_proj": 32
|
| 34 |
+
},
|
| 35 |
+
"revision": null,
|
| 36 |
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"target_modules": [
|
| 37 |
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"v_proj",
|
| 38 |
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"k_proj",
|
| 39 |
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"router",
|
| 40 |
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"q_proj",
|
| 41 |
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"o_proj"
|
| 42 |
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],
|
| 43 |
+
"target_parameters": [
|
| 44 |
+
"down_proj",
|
| 45 |
+
"gate.weight",
|
| 46 |
+
"gate_up_proj"
|
| 47 |
+
],
|
| 48 |
+
"task_type": "CAUSAL_LM",
|
| 49 |
+
"trainable_token_indices": null,
|
| 50 |
+
"use_bdlora": null,
|
| 51 |
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"use_dora": false,
|
| 52 |
+
"use_qalora": false,
|
| 53 |
+
"use_rslora": false
|
| 54 |
+
}
|
adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:534d29b64dad88ce695bb9e73b97fc233845b208ce3e55651fb4dc18289e50d5
|
| 3 |
+
size 757167808
|
chat_template.jinja
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
| 1 |
+
{{ bos_token }}{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>
|
| 2 |
+
' + message['content'] + '
|
| 3 |
+
' }}{% elif message['role'] == 'user' %}{{ '<|user|>
|
| 4 |
+
' + message['content'] + '
|
| 5 |
+
' }}{% elif message['role'] == 'assistant' %}{% if not loop.last %}{{ '<|assistant|>
|
| 6 |
+
' + message['content'] + eos_token + '
|
| 7 |
+
' }}{% else %}{{ '<|assistant|>
|
| 8 |
+
' + message['content'] + eos_token }}{% endif %}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|assistant|>
|
| 9 |
+
' }}{% endif %}{% endfor %}
|
optimizer.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
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|
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|
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|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:2a1a496d5cde964656971d6bd3ff487c838bac180fbb7158415f23be63326ebe
|
| 3 |
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size 1514466827
|
rng_state_0.pth
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:0f3851eaeaf25c01cdf035d039e640de9f9c6100893265923d5b097334e8c112
|
| 3 |
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size 15429
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rng_state_1.pth
ADDED
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@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:2db99be7740cf13f5e1d5ecfb9b5a7b02d9f84f0d1514686191b17a6d8aa1558
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| 3 |
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size 15365
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rng_state_2.pth
ADDED
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:945d36e6c014a16aa7533cbe84091de66cd2a1ca925efdf85bb7a13f48fe9746
|
| 3 |
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size 15429
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rng_state_3.pth
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:6f891dea82c2c9936cfbb754821e1d7ea225defd78bf20fb0491c0dc0dd68b94
|
| 3 |
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size 15429
|
scheduler.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
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|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:4dcd0fcafdf0cac56c6781c9554680e5d2ba9bfee0d823975791d1d648a00172
|
| 3 |
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size 1465
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,15 @@
|
|
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|
| 1 |
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{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"backend": "tokenizers",
|
| 4 |
+
"bos_token": "|||IP_ADDRESS|||",
|
| 5 |
+
"clean_up_tokenization_spaces": false,
|
| 6 |
+
"eos_token": "|||IP_ADDRESS|||",
|
| 7 |
+
"errors": "replace",
|
| 8 |
+
"is_local": false,
|
| 9 |
+
"local_files_only": true,
|
| 10 |
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"model_max_length": 1000000000000000019884624838656,
|
| 11 |
+
"pad_token": "<pad>",
|
| 12 |
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"tokenizer_class": "GPTNeoXTokenizer",
|
| 13 |
+
"trim_offsets": true,
|
| 14 |
+
"unk_token": null
|
| 15 |
+
}
|
trainer_state.json
ADDED
|
@@ -0,0 +1,614 @@
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|
| 1 |
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{
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| 2 |
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"best_global_step": null,
|
| 3 |
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|
| 4 |
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|
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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"is_world_process_zero": true,
|
| 11 |
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"log_history": [
|
| 12 |
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{
|
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