Move field table to vignette; add app screenshot to README

- Replaces the 25-row field table in README with a brief summary and
  link to the rendered vignette, which now carries the full table in
  the Python API section
- Adds streamlitapp.png screenshot to README
- Gitignores README.html, README_files/, and *.xlsx to prevent
  accidental commits of render artefacts and local files

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: erwardenaar

.gitignore

@@ -11,3 +11,6 @@ docs/vignette_files/
 docs/code_walkthrough.md
 docs/code_walkthrough.html
 docs/code_walkthrough_files/
+README.html
+README_files/
+*.xlsx

README.md

@@ -8,34 +8,7 @@ Choosing the right open LLM for research is hard given the rapidly growing lands
 
 ## Database fields
 
-Each model record contains 25 fields:
-
-| Field | Type | Description |
-|---|---|---|
-| `name`, `family`, `organization`, `country_of_origin` | str | Model identity |
-| `release_year` | int | Year of public release |
-| `size_b` | float | Model size in billions of parameters |
-| `training_tokens_b` | float \| None | Pre-training token count in billions; `None` when undisclosed |
-| `context_window` | int | Maximum context length in tokens |
-| `modality` | list[str] | Supported modalities (`"text"` and/or `"image"`) |
-| `architecture` | str | `decoder-only`, `encoder-decoder`, or `mixture-of-experts` |
-| `license` | str | License name |
-| `open_weights` | bool | Model weights are publicly available |
-| `open_training_data` | bool | Training data is publicly available |
-| `intermediate_checkpoints` | bool | Intermediate training checkpoints have been released |
-| `open_code` | bool | Training code is publicly available |
-| `multilingual` | bool | Officially supports more than one language |
-| `num_languages` | int | Number of officially supported languages |
-| `languages` | list[str] | Names of officially supported languages |
-| `has_instruct_version` | bool | An instruction-tuned variant exists (or the model is itself instruction-tuned) |
-| `model_type` | str | Model release type: `"base"`, `"instruct"`, or `"reasoning"` |
-| `has_think_version` | bool | A chain-of-thought / think variant exists (or the model is itself a reasoning model) |
-| `notes` | str *(optional)* | Additional context; present only for models where extra clarification is needed (e.g. post-trained models where `training_tokens_b` is null for structural reasons) |
-| `foundational_paper` | str | URL of the foundational paper (arXiv for most models; non-arXiv for GPT-J 6B, Grok-1, Mixtral 8x22B, and Sarvam 30B) |
-| `huggingface_id` | str | HuggingFace model identifier |
-| `openness_score` | int | Computed 0–5 score: sum of `open_weights` + `open_training_data` + `intermediate_checkpoints` + `open_code` + permissive license (Apache 2.0 or MIT) |
-
-Languages reflect officially supported languages as documented by the model creators, not partial or limited capabilities (e.g. Falcon supports German, Spanish and French officially, but has only limited capabilities in several other languages which are not included).
+Each model record contains 25 fields covering identity, size, training scale, context window, modality, architecture, license, openness flags, language support, and links to the foundational paper and HuggingFace page. Most records are base models; a small number are instruct or reasoning variants. See the [rendered vignette](https://htmlpreview.github.io/?https://github.com/Programming-The-Next-Step-2026/openllm-selector/blob/week-4/docs/vignette.html) for the full field reference.
 
 ## Installation
 
@@ -49,6 +22,8 @@ To run the interactive Streamlit app locally:
 streamlit run app/app.py
 ```
 
+![openllm-selector Streamlit app](streamlitapp.png)
+
 ## Python API
 
 ```python

docs/vignette.qmd

@@ -22,9 +22,14 @@ An optional `notes` field is present for models that require additional context,
 pip install git+https://github.com/Programming-The-Next-Step-2026/openllm-selector.git@week-4
 ```
 
-This gives access to the Python API (`load_models`, `filter_models`, and related functions) without any further steps. To run the interactive Streamlit app, clone the repository and launch it from the project root:
+This gives access to the Python API (`load_models`, `filter_models`, and related functions) without any further steps. 
+
+To run the interactive Streamlit app, clone the repository and launch it from the project root:
 
 ```bash
+git clone https://github.com/Programming-The-Next-Step-2026/openllm-selector.git
+cd openllm-selector
+git checkout week-4
 streamlit run app/app.py
 ```
 
@@ -214,6 +219,33 @@ The same five questions answered in code. All functions are importable directly
 import openllm_selector as o
 ```
 
+Each model dict contains the following fields:
+
+| Field | Type | Description |
+|---|---|---|
+| `name`, `family`, `organization`, `country_of_origin` | str | Model identity |
+| `release_year` | int | Year of public release |
+| `size_b` | float | Model size in billions of parameters |
+| `training_tokens_b` | float \| None | Pre-training token count in billions; `None` when undisclosed |
+| `context_window` | int | Maximum context length in tokens |
+| `modality` | list[str] | Supported modalities (`"text"` and/or `"image"`) |
+| `architecture` | str | `decoder-only`, `encoder-decoder`, or `mixture-of-experts` |
+| `license` | str | License name |
+| `open_weights` | bool | Model weights are publicly available |
+| `open_training_data` | bool | Training data is publicly available |
+| `intermediate_checkpoints` | bool | Intermediate training checkpoints have been released |
+| `open_code` | bool | Training code is publicly available |
+| `multilingual` | bool | Officially supports more than one language |
+| `num_languages` | int | Number of officially supported languages |
+| `languages` | list[str] | Names of officially supported languages |
+| `has_instruct_version` | bool | An instruction-tuned variant exists (or the model is itself instruction-tuned) |
+| `model_type` | str | Model release type: `"base"`, `"instruct"`, or `"reasoning"` |
+| `has_think_version` | bool | A chain-of-thought / think variant exists (or the model is itself a reasoning model) |
+| `notes` | str *(optional)* | Additional context; present only for models where extra clarification is needed (e.g. post-trained models where `training_tokens_b` is null for structural reasons) |
+| `foundational_paper` | str | URL of the foundational paper (arXiv for most models; non-arXiv for GPT-J 6B, Grok-1, Mixtral 8x22B, and Sarvam 30B) |
+| `huggingface_id` | str | HuggingFace model identifier |
+| `openness_score` | int | Computed 0–5 score: sum of `open_weights` + `open_training_data` + `intermediate_checkpoints` + `open_code` + permissive license (Apache 2.0 or MIT) |
+
 ---
 
 ### Scenario a — Training dynamics researcher