---
library_name: transformers
license: apache-2.0
datasets:
- crumb/askmistral-pile-2-15
language:
- en
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** me
- **Model type:** Mistral
- **Language(s) (NLP):** en
- **License:** apache

## Uses

general web text completions at extremely low resource use

### Out-of-Scope Use

not an instruct model

## Bias, Risks, and Limitations

trained on web text, though filtered no guarantees theres not toxic stuff in there

## How to Get Started with the Model

Use the code below to get started with the model.

```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

[crumb/askmistral-pile-2-15](https://huggingface.co/datasets/crumb/askmistral-pile-2-15)

### Training Procedure 

| 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]

train_runtime 62541.9424

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|

#### Summary

## Model Examination [optional]

its ok

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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).

- **Hardware Type:** A6000
- **Hours used:** 34.74
- **Cloud Provider:** n/a
- **Compute Region** iowa
- **Carbon Emitted:** 4.5kg CO2eq.

## Technical Specifications [optional]

### Model Architecture and Objective

mistral, causal language modelling

### Compute Infrastructure

what

#### Hardware

lambda vector 2xA6000

#### Software

huggingface transformers / pytorch / custom trainer

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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