Instructions to use ddidacus/phi-tiny-moe-prompt-conditioned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ddidacus/phi-tiny-moe-prompt-conditioned with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-tiny-MoE-instruct") model = PeftModel.from_pretrained(base_model, "ddidacus/phi-tiny-moe-prompt-conditioned") - Transformers
How to use ddidacus/phi-tiny-moe-prompt-conditioned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ddidacus/phi-tiny-moe-prompt-conditioned") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ddidacus/phi-tiny-moe-prompt-conditioned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ddidacus/phi-tiny-moe-prompt-conditioned with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ddidacus/phi-tiny-moe-prompt-conditioned" # 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/phi-tiny-moe-prompt-conditioned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ddidacus/phi-tiny-moe-prompt-conditioned
- SGLang
How to use ddidacus/phi-tiny-moe-prompt-conditioned 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/phi-tiny-moe-prompt-conditioned" \ --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/phi-tiny-moe-prompt-conditioned", "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/phi-tiny-moe-prompt-conditioned" \ --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/phi-tiny-moe-prompt-conditioned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ddidacus/phi-tiny-moe-prompt-conditioned with Docker Model Runner:
docker model run hf.co/ddidacus/phi-tiny-moe-prompt-conditioned
Upload folder using huggingface_hub
Browse files- README.md +209 -0
- adapter_config.json +56 -0
- adapter_model.safetensors +3 -0
- chat_template.jinja +4 -0
- optimizer.pt +3 -0
- rng_state.pth +3 -0
- scheduler.pt +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +16 -0
- trainer_state.json +674 -0
- training_args.bin +3 -0
README.md
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| 1 |
+
---
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| 2 |
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base_model: microsoft/Phi-tiny-MoE-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:microsoft/Phi-tiny-MoE-instruct
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- grpo
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- lora
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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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- **Model type:** [More Information Needed]
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| 31 |
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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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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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| 52 |
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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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| 58 |
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| 59 |
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### Out-of-Scope Use
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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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[More Information Needed]
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| 64 |
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## Bias, Risks, and Limitations
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| 66 |
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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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| 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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<!-- 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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| 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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- **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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- **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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[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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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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| 189 |
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## Glossary [optional]
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| 191 |
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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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[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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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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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 |
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},
|
| 6 |
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"arrow_config": null,
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| 7 |
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"auto_mapping": null,
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| 8 |
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"base_model_name_or_path": "microsoft/Phi-tiny-MoE-instruct",
|
| 9 |
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"bias": "none",
|
| 10 |
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"corda_config": null,
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| 11 |
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"ensure_weight_tying": false,
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| 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",
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| 30 |
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"qalora_group_size": 16,
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| 31 |
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"r": 16,
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| 32 |
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"rank_pattern": {
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| 33 |
+
".*\\.gate_up_proj": 32
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| 34 |
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},
|
| 35 |
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"revision": null,
|
| 36 |
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"target_modules": [
|
| 37 |
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"down_proj",
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| 38 |
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"gate_proj",
|
| 39 |
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"k_proj",
|
| 40 |
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"o_proj",
|
| 41 |
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"up_proj",
|
| 42 |
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"q_proj",
|
| 43 |
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"v_proj"
|
| 44 |
+
],
|
| 45 |
+
"target_parameters": [
|
| 46 |
+
"experts.gate_up_proj",
|
| 47 |
+
"experts.down_proj",
|
| 48 |
+
"router.weight"
|
| 49 |
+
],
|
| 50 |
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"task_type": "CAUSAL_LM",
|
| 51 |
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"trainable_token_indices": null,
|
| 52 |
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"use_bdlora": null,
|
| 53 |
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"use_dora": false,
|
| 54 |
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"use_qalora": false,
|
| 55 |
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"use_rslora": false
|
| 56 |
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}
|
adapter_model.safetensors
ADDED
|
@@ -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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| 3 |
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size 528575792
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chat_template.jinja
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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{% for message in messages %}{{'<|' + message['role'] + '|>' + '
|
| 2 |
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|
| 3 |
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' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>
|
| 4 |
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' }}{% else %}{{ eos_token }}{% endif %}
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optimizer.pt
ADDED
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|
| 1 |
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version https://git-lfs.github.com/spec/v1
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|
| 3 |
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size 1057414883
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rng_state.pth
ADDED
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:9b5173e684b4917b93bcba98ed71d9fdf2d7ff47a9655f0739a371a7ff79d0af
|
| 3 |
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size 14645
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scheduler.pt
ADDED
|
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 1465
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tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,16 @@
|
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| 1 |
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{
|
| 2 |
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"backend": "tokenizers",
|
| 3 |
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"bos_token": "<s>",
|
| 4 |
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"clean_up_tokenization_spaces": false,
|
| 5 |
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"eos_token": "<|endoftext|>",
|
| 6 |
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"is_local": false,
|
| 7 |
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"legacy": false,
|
| 8 |
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"local_files_only": true,
|
| 9 |
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"model_max_length": 4096,
|
| 10 |
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"pad_token": "<|endoftext|>",
|
| 11 |
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"padding_side": "left",
|
| 12 |
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"sp_model_kwargs": {},
|
| 13 |
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"tokenizer_class": "TokenizersBackend",
|
| 14 |
+
"unk_token": "<unk>",
|
| 15 |
+
"use_default_system_prompt": false
|
| 16 |
+
}
|
trainer_state.json
ADDED
|
@@ -0,0 +1,674 @@
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| 1 |
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{
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| 2 |
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| 3 |
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|
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|
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|
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| 11 |
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| 12 |
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| 652 |
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| 660 |
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| 665 |
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| 666 |
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| 667 |
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| 668 |
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| 669 |
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| 674 |
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|
training_args.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:6b89a718814f591547f6f8a4a51e148f73588c1b7d7497e90a05db37bbc2325a
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| 3 |
+
size 7825
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