neph1
/

llama-3-instruct-bellman-8b-swedish

@@ -12,219 +12,48 @@ datasets:
 # Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 Training run on 240606:
-Step 	Training Loss 	Validation Loss
- 25 	1.506400 	1.164538
- 50 	1.128500 	1.059316
- 75 	1.095100 	1.040511
-100 	1.068700 	1.031033
-125 	1.061300 	1.024377
-150 	1.035700 	1.017490
-175 	1.061200 	1.012095
-200 	1.031600 	1.007867
-225 	1.031900 	1.002652
-250 	0.958300 	1.003817
-275 	0.967900 	1.000483
-300 	0.950000 	0.998807
-325 	0.974300 	0.996894
-350 	0.960700 	0.994098
-375 	0.956000 	0.991491
-400 	0.940500 	0.988697
-425 	0.949100 	0.987253
-450 	0.940600 	0.986425 <-- Picked checkpoint
-475 	0.888300 	0.994204
-500 	0.881700 	0.994897
 ### 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:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### 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. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## 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:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## 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]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
 ## Model Card Contact
-[More Information Needed]

 # Model Card for Model ID
+This version of bellman is finetuned from llama-3-instruct-8b.
+It is arguable whether it's better at Swedish, because llama-3 is really good. It's however finetuned for prompt question answering, based on a dataset created from
+Swedish wikipedia, with a lot of Sweden-centric questions.
+New from previous versions is questions from a translated code-feedback dataset, as well as a number of stories. It's not great at generating stories,
+but better than previosly.
 ## Model Details
 Training run on 240606:
+Step 	Training Loss 	Validation Loss<br>
+ 25 	1.506400 	1.164538<br>
+ 50 	1.128500 	1.059316<br>
+ 75 	1.095100 	1.040511<br>
+100 	1.068700 	1.031033<br>
+125 	1.061300 	1.024377<br>
+150 	1.035700 	1.017490<br>
+175 	1.061200 	1.012095<br>
+200 	1.031600 	1.007867<br>
+225 	1.031900 	1.002652<br>
+250 	0.958300 	1.003817<br>
+275 	0.967900 	1.000483<br>
+300 	0.950000 	0.998807<br>
+325 	0.974300 	0.996894<br>
+350 	0.960700 	0.994098<br>
+375 	0.956000 	0.991491<br>
+400 	0.940500 	0.988697<br>
+425 	0.949100 	0.987253<br>
+450 	0.940600 	0.986425 <-- Picked checkpoint<br>
+475 	0.888300 	0.994204<br>
+500 	0.881700 	0.994897<br>
 ### Model Description
+- **Developed by:** Me
+- **Funded by:** Me
+- **Model type:** Instruct
+- **Language(s) (NLP):** Swedish
+- **License:** llama-3
+- **Finetuned from model:** Llama3 Instruct 8b
 ## Model Card Contact
+[email protected]