| --- |
| library_name: transformers |
| license: apache-2.0 |
| datasets: |
| - crumb/askmistral-pile-2-15 |
| language: |
| - en |
| --- |
| |
| # 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 |
| - **Model type:** Mistral |
| - **Language(s) (NLP):** en |
| - **License:** apache |
|
|
| ## 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 |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model = AutoModelForCausalLM.from_pretrained("crumb/nano-mistral") |
| tokenizer = AutoTokenizer.from_pretrained("crumb/nano-mistral") |
| |
| inputs = tokenizer(["Once upon a time,"], return_tensors="pt") |
| inputs = {k:v.to(model.device) for k,v in dict(inputs).items()} |
| outputs = model.generate(inputs, max_new_tokens=128, temperature=0.7, top_k=20, do_sample=True) |
| outputs = tokenizer.batch_decode(outputs) |
| for i in outputs: |
| print(i) |
| ``` |
|
|
| ## Training Details |
|
|
| ### 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 | |
| | - | - | |
| | Context Length | 2048 | |
| | Batch Size | 128 | |
| | Learning Rate | 6e-4 | |
| | Scheduler | One-Cycle | |
| | Adam eps | 1e-8 | |
| | Adam beta1 | 0.9 | |
| | Adam beta2 | 0.95 | |
| | Weight Decay | 0.1 | |
| | Max Grad Norm | 1.0 | |
| | Optimizer | adamw_torch | |
| | Tokens | 3,401,640,960 | |
| |
| #### Preprocessing [optional] |
| |
| [More Information Needed] |
| |
| |
| #### Training Hyperparameters |
| |
| - **Training regime:** bf16 non-mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
| |
| #### Speeds, Sizes, Times [optional] |
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| train_runtime 62541.9424 |
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| train_samples_per_second 26.557 |
| |
| [More Information Needed] |
| |
| ## Evaluation |
| |
| ### Testing Data, Factors & Metrics |
| |
| #### Testing Data |
| |
| held out set of [crumb/askmistral-pile-2-15](https://huggingface.co/datasets/crumb/askmistral-pile-2-15) |
| |
| #### Factors |
| |
| <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
| |
| [More Information Needed] |
| |
| #### Metrics |
| |
| <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
| |
| open llm leaderboard eval datasets and settings |
| |
| ### Results |
| |
| OpenLLM Leaderboard Mean Score + Stderr: |
| (29.30, 0.42) |
| |
| | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |
| |-------------|------:|------|-----:|--------|-----:|---|-----:| |
| |arc_challenge| 1|none | 25|acc |0.1843|± |0.0113| |
| | | |none | 25|acc_norm|0.2167|± |0.0120| |
| |truthfulqa_mc2| 2|none | 0|acc |0.4719|± |0.0156| |
| |winogrande| 1|none | 5|acc |0.517|± | 0.014| |
| |hellaswag| 1|none | 10|acc |0.2803|± |0.0045| |
| | | |none | 10|acc_norm|0.2886|± |0.0045| |
| |gsm8k| 3|strict-match | 5|exact_match|0.0008|± |0.0008| |
| | | |flexible-extract| 5|exact_match|0.0099|± |0.0027| |
| |
| #### MMLU |
| |
| value, stderr = (0.253980701754386, 0.004428598058450528) |
| | Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |
| |-----------------------------------|------:|------|-----:|------|-----:|---|-----:| |
| |world_religions | 0|none | 5|acc |0.2222|± |0.0319| |
| |virology | 0|none | 5|acc |0.2711|± |0.0346| |
| |us_foreign_policy | 0|none | 5|acc |0.3300|± |0.0473| |
| |sociology | 0|none | 5|acc |0.2388|± |0.0301| |
| |security_studies | 0|none | 5|acc |0.2367|± |0.0272| |
| |public_relations | 0|none | 5|acc |0.2273|± |0.0401| |
| |professional_psychology | 0|none | 5|acc |0.2484|± |0.0175| |
| |professional_medicine | 0|none | 5|acc |0.4596|± |0.0303| |
| |professional_law | 0|none | 5|acc |0.2464|± |0.0110| |
| |professional_accounting | 0|none | 5|acc |0.2021|± |0.0240| |
| |prehistory | 0|none | 5|acc |0.2130|± |0.0228| |
| |philosophy | 0|none | 5|acc |0.2219|± |0.0236| |
| |nutrition | 0|none | 5|acc |0.2157|± |0.0236| |
| |moral_scenarios | 0|none | 5|acc |0.2380|± |0.0142| |
| |moral_disputes | 0|none | 5|acc |0.2486|± |0.0233| |
| |miscellaneous | 0|none | 5|acc |0.2516|± |0.0155| |
| |medical_genetics | 0|none | 5|acc |0.3000|± |0.0461| |
| |marketing | 0|none | 5|acc |0.2265|± |0.0274| |
| |management | 0|none | 5|acc |0.1748|± |0.0376| |
| |machine_learning | 0|none | 5|acc |0.3125|± |0.0440| |
| |logical_fallacies | 0|none | 5|acc |0.2393|± |0.0335| |
| |jurisprudence | 0|none | 5|acc |0.2315|± |0.0408| |
| |international_law | 0|none | 5|acc |0.3140|± |0.0424| |
| |human_sexuality | 0|none | 5|acc |0.2519|± |0.0381| |
| |human_aging | 0|none | 5|acc |0.3049|± |0.0309| |
| |high_school_world_history | 0|none | 5|acc |0.2658|± |0.0288| |
| |high_school_us_history | 0|none | 5|acc |0.2451|± |0.0302| |
| |high_school_statistics | 0|none | 5|acc |0.4722|± |0.0340| |
| |high_school_psychology | 0|none | 5|acc |0.1963|± |0.0170| |
| |high_school_physics | 0|none | 5|acc |0.3046|± |0.0376| |
| |high_school_microeconomics | 0|none | 5|acc |0.2773|± |0.0291| |
| |high_school_mathematics | 0|none | 5|acc |0.2667|± |0.0270| |
| |high_school_macroeconomics | 0|none | 5|acc |0.2667|± |0.0224| |
| |high_school_government_and_politics| 0|none | 5|acc |0.2591|± |0.0316| |
| |high_school_geography | 0|none | 5|acc |0.2424|± |0.0305| |
| |high_school_european_history | 0|none | 5|acc |0.2242|± |0.0326| |
| |high_school_computer_science | 0|none | 5|acc |0.2800|± |0.0451| |
| |high_school_chemistry | 0|none | 5|acc |0.2857|± |0.0318| |
| |high_school_biology | 0|none | 5|acc |0.3129|± |0.0264| |
| |global_facts | 0|none | 5|acc |0.1500|± |0.0359| |
| |formal_logic | 0|none | 5|acc |0.1905|± |0.0351| |
| |elementary_mathematics | 0|none | 5|acc |0.2513|± |0.0223| |
| |electrical_engineering | 0|none | 5|acc |0.2759|± |0.0372| |
| |econometrics | 0|none | 5|acc |0.2456|± |0.0405| |
| |conceptual_physics | 0|none | 5|acc |0.2638|± |0.0288| |
| |computer_security | 0|none | 5|acc |0.1800|± |0.0386| |
| |college_physics | 0|none | 5|acc |0.2549|± |0.0434| |
| |college_medicine | 0|none | 5|acc |0.2023|± |0.0306| |
| |college_mathematics | 0|none | 5|acc |0.2900|± |0.0456| |
| |college_computer_science | 0|none | 5|acc |0.2700|± |0.0446| |
| |college_chemistry | 0|none | 5|acc |0.2500|± |0.0435| |
| |college_biology | 0|none | 5|acc |0.2222|± |0.0348| |
| |clinical_knowledge | 0|none | 5|acc |0.2377|± |0.0262| |
| |business_ethics | 0|none | 5|acc |0.2100|± |0.0409| |
| |astronomy | 0|none | 5|acc |0.1776|± |0.0311| |
| |anatomy | 0|none | 5|acc |0.2593|± |0.0379| |
| |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 |
| - **Hours used:** 34.74 |
| - **Cloud Provider:** n/a |
| - **Compute Region** iowa |
| - **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] |
