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--- |
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library_name: transformers |
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license: apache-2.0 |
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datasets: |
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- crumb/askmistral-pile-2-15 |
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language: |
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- en |
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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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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:** me |
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- **Model type:** Mistral |
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- **Language(s) (NLP):** en |
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- **License:** apache |
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## Uses |
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general web text completions at extremely low resource use |
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### Out-of-Scope Use |
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not an instruct model |
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## Bias, Risks, and Limitations |
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trained on web text, though filtered no guarantees theres not toxic stuff in there |
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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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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("crumb/nano-mistral") |
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tokenizer = AutoTokenizer.from_pretrained("crumb/nano-mistral") |
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inputs = tokenizer(["Once upon a time,"], return_tensors="pt") |
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inputs = {k:v.to(model.device) for k,v in dict(inputs).items()} |
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outputs = model.generate(inputs, max_new_tokens=128, temperature=0.7, top_k=20, do_sample=True) |
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outputs = tokenizer.batch_decode(outputs) |
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for i in outputs: |
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print(i) |
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``` |
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## Training Details |
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### Training Data |
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[crumb/askmistral-pile-2-15](https://huggingface.co/datasets/crumb/askmistral-pile-2-15) |
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### Training Procedure |
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| Parameter | Value | |
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| - | - | |
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| Context Length | 2048 | |
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| Batch Size | 128 | |
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| Learning Rate | 6e-4 | |
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| Scheduler | One-Cycle | |
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| Adam eps | 1e-8 | |
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| Adam beta1 | 0.9 | |
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| Adam beta2 | 0.95 | |
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| Weight Decay | 0.1 | |
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| Max Grad Norm | 1.0 | |
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| Optimizer | adamw_torch | |
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| Tokens | 3,401,640,960 | |
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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:** bf16 non-mixed precision <!--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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train_runtime 62541.9424 |
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train_samples_per_second 26.557 |
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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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held out set of [crumb/askmistral-pile-2-15](https://huggingface.co/datasets/crumb/askmistral-pile-2-15) |
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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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open llm leaderboard eval datasets and settings |
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### Results |
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OpenLLM Leaderboard Mean Score + Stderr: |
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(29.30, 0.42) |
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| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |
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|-------------|------:|------|-----:|--------|-----:|---|-----:| |
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|arc_challenge| 1|none | 25|acc |0.1843|± |0.0113| |
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| | |none | 25|acc_norm|0.2167|± |0.0120| |
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|truthfulqa_mc2| 2|none | 0|acc |0.4719|± |0.0156| |
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|winogrande| 1|none | 5|acc |0.517|± | 0.014| |
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|hellaswag| 1|none | 10|acc |0.2803|± |0.0045| |
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| | |none | 10|acc_norm|0.2886|± |0.0045| |
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|gsm8k| 3|strict-match | 5|exact_match|0.0008|± |0.0008| |
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| | |flexible-extract| 5|exact_match|0.0099|± |0.0027| |
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#### MMLU |
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value, stderr = (0.253980701754386, 0.004428598058450528) |
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| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |
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|-----------------------------------|------:|------|-----:|------|-----:|---|-----:| |
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|world_religions | 0|none | 5|acc |0.2222|± |0.0319| |
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|virology | 0|none | 5|acc |0.2711|± |0.0346| |
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|us_foreign_policy | 0|none | 5|acc |0.3300|± |0.0473| |
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|sociology | 0|none | 5|acc |0.2388|± |0.0301| |
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|security_studies | 0|none | 5|acc |0.2367|± |0.0272| |
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|public_relations | 0|none | 5|acc |0.2273|± |0.0401| |
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|professional_psychology | 0|none | 5|acc |0.2484|± |0.0175| |
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|professional_medicine | 0|none | 5|acc |0.4596|± |0.0303| |
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|professional_law | 0|none | 5|acc |0.2464|± |0.0110| |
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|professional_accounting | 0|none | 5|acc |0.2021|± |0.0240| |
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|prehistory | 0|none | 5|acc |0.2130|± |0.0228| |
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|philosophy | 0|none | 5|acc |0.2219|± |0.0236| |
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|nutrition | 0|none | 5|acc |0.2157|± |0.0236| |
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|moral_scenarios | 0|none | 5|acc |0.2380|± |0.0142| |
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|moral_disputes | 0|none | 5|acc |0.2486|± |0.0233| |
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|miscellaneous | 0|none | 5|acc |0.2516|± |0.0155| |
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|medical_genetics | 0|none | 5|acc |0.3000|± |0.0461| |
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|marketing | 0|none | 5|acc |0.2265|± |0.0274| |
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|management | 0|none | 5|acc |0.1748|± |0.0376| |
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|machine_learning | 0|none | 5|acc |0.3125|± |0.0440| |
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|logical_fallacies | 0|none | 5|acc |0.2393|± |0.0335| |
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|jurisprudence | 0|none | 5|acc |0.2315|± |0.0408| |
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|international_law | 0|none | 5|acc |0.3140|± |0.0424| |
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|human_sexuality | 0|none | 5|acc |0.2519|± |0.0381| |
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|human_aging | 0|none | 5|acc |0.3049|± |0.0309| |
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|high_school_world_history | 0|none | 5|acc |0.2658|± |0.0288| |
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|high_school_us_history | 0|none | 5|acc |0.2451|± |0.0302| |
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|high_school_statistics | 0|none | 5|acc |0.4722|± |0.0340| |
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|high_school_psychology | 0|none | 5|acc |0.1963|± |0.0170| |
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|high_school_physics | 0|none | 5|acc |0.3046|± |0.0376| |
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|high_school_microeconomics | 0|none | 5|acc |0.2773|± |0.0291| |
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|high_school_mathematics | 0|none | 5|acc |0.2667|± |0.0270| |
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|high_school_macroeconomics | 0|none | 5|acc |0.2667|± |0.0224| |
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|high_school_government_and_politics| 0|none | 5|acc |0.2591|± |0.0316| |
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|high_school_geography | 0|none | 5|acc |0.2424|± |0.0305| |
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|high_school_european_history | 0|none | 5|acc |0.2242|± |0.0326| |
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|high_school_computer_science | 0|none | 5|acc |0.2800|± |0.0451| |
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|high_school_chemistry | 0|none | 5|acc |0.2857|± |0.0318| |
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|high_school_biology | 0|none | 5|acc |0.3129|± |0.0264| |
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|global_facts | 0|none | 5|acc |0.1500|± |0.0359| |
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|formal_logic | 0|none | 5|acc |0.1905|± |0.0351| |
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|elementary_mathematics | 0|none | 5|acc |0.2513|± |0.0223| |
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|electrical_engineering | 0|none | 5|acc |0.2759|± |0.0372| |
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|econometrics | 0|none | 5|acc |0.2456|± |0.0405| |
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|conceptual_physics | 0|none | 5|acc |0.2638|± |0.0288| |
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|computer_security | 0|none | 5|acc |0.1800|± |0.0386| |
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|college_physics | 0|none | 5|acc |0.2549|± |0.0434| |
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|college_medicine | 0|none | 5|acc |0.2023|± |0.0306| |
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|college_mathematics | 0|none | 5|acc |0.2900|± |0.0456| |
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|college_computer_science | 0|none | 5|acc |0.2700|± |0.0446| |
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|college_chemistry | 0|none | 5|acc |0.2500|± |0.0435| |
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|college_biology | 0|none | 5|acc |0.2222|± |0.0348| |
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|clinical_knowledge | 0|none | 5|acc |0.2377|± |0.0262| |
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|business_ethics | 0|none | 5|acc |0.2100|± |0.0409| |
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|astronomy | 0|none | 5|acc |0.1776|± |0.0311| |
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|anatomy | 0|none | 5|acc |0.2593|± |0.0379| |
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|abstract_algebra | 0|none | 5|acc |0.2200|± |0.0416| |
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#### Summary |
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## Model Examination [optional] |
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its ok |
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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:** A6000 |
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- **Hours used:** 34.74 |
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- **Cloud Provider:** n/a |
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- **Compute Region** iowa |
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- **Carbon Emitted:** 4.5kg CO2eq. |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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mistral, causal language modelling |
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### Compute Infrastructure |
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what |
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#### Hardware |
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lambda vector 2xA6000 |
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#### Software |
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huggingface transformers / pytorch / custom trainer |
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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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## Glossary [optional] |
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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] |
