Update active EN LoRA adapter (step 2400/2500)

en/README.md

@@ -1,256 +1,210 @@
 ---
-language:
-- en
-- ru
-license: apache-2.0
 base_model: unsloth/Ministral-3-3B-Instruct-2512-bnb-4bit
+library_name: peft
+pipeline_tag: text-generation
 tags:
-- reasoning
-- r1
-- deepseek-r1
-- ministral
-- gguf
-- cot
-- chain-of-thought
-- english
-- code
-- math
-- text-generation
+- base_model:adapter:unsloth/Ministral-3-3B-Instruct-2512-bnb-4bit
+- lora
+- sft
+- transformers
+- trl
 - unsloth
-- llama.cpp
-- lmstudio
-- ollama
-- vllm
-- jan
-- openclaw
-- hermes-agent
-datasets:
-- fwizzer1/en-deepthink-mega
-pipeline_tag: text-generation
-widget:
-- text: "[SYSTEM_PROMPT]You are a thinking neural network and your name is Fwizzer-R1-3B-EN. Write your entire train of thought and steps inside the <think> and </think> tags, and the final answer must be written after them.[/SYSTEM_PROMPT][INST] Who are you and what are your core capabilities? [/INST]"
-  example_title: "Who are you? (Identity)"
-- text: "[SYSTEM_PROMPT]You are a thinking neural network and your name is Fwizzer-R1-3B-EN. Write your entire train of thought and steps inside the <think> and </think> tags, and the final answer must be written after them.[/SYSTEM_PROMPT][INST] A farmer has chickens and cows. Total 30 heads and 88 legs. How many chickens and how many cows does he have? [/INST]"
-  example_title: "Logic & Math Problem"
-- text: "[SYSTEM_PROMPT]You are a thinking neural network and your name is Fwizzer-R1-3B-EN. Write your entire train of thought and steps inside the <think> and </think> tags, and the final answer must be written after them.[/SYSTEM_PROMPT][INST] Implement an LRU Cache in Python with O(1) get and put operations. [/INST]"
-  example_title: "Python Data Structures"
-model-index:
-- name: Fwizzer-R1-3B-EN-v2
-  results:
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: GSM8K
-      type: gsm8k
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 84.1
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: MATH
-      type: math
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 56.3
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: HumanEval
-      type: humaneval
-    metrics:
-    - name: Pass@1
-      type: pass@1
-      value: 65.2
 ---
 
-<div align="center">
+# Model Card for Model ID
 
-# 🇬🇧 🧠 Fwizzer-R1-3B-EN-v2
-### Advanced English Reasoning & Coding Compact Language Model
-#### DeepThink Step-by-Step Chain-of-Thought on Mistral-3B Architecture
+<!-- Provide a quick summary of what the model is/does. -->
 
-[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-fwizzer1%2FFwizzer--R1--3B--EN--v2-blue)](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-EN-v2)
-[![Base Model](https://img.shields.io/badge/Base%20Model-Ministral--3B-orange)](https://huggingface.co/mistralai/Ministral-3-3B-Instruct-2512)
-[![Parameters](https://img.shields.io/badge/Parameters-3.29B-yellow)](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-EN-v2)
-[![Context](https://img.shields.io/badge/Context-32K%20Tokens-purple)](https://huggingface.co/fwizzer1/Fwizzer-R1-3B-EN-v2)
-[![License](https://img.shields.io/badge/License-Apache%202.0-green)](https://www.apache.org/licenses/LICENSE-2.0)
-[![Dataset](https://img.shields.io/badge/Dataset-en--deepthink--mega-red)](https://huggingface.co/datasets/fwizzer1/en-deepthink-mega)
 
-[Overview](#-overview) • [Quickstart](#-quickstart) • [14-Platform Guide](#-14-platform-universal-guide) • [Benchmarks](#-benchmarks) • [Quantization](#-gguf-quantization)
 
-</div>
+## Model Details
 
----
+### Model Description
 
-## 🌟 Overview
+<!-- Provide a longer summary of what this model is. -->
 
-**Fwizzer-R1-3B-EN-v2** is a specialized English reasoning model (3.29B parameters) fine-tuned on the massive [`fwizzer1/en-deepthink-mega`](https://huggingface.co/datasets/fwizzer1/en-deepthink-mega) dataset (17,281 verified Chain-of-Thought reasoning pairs).
 
-Trained with LoRA on Mistral AI's cutting-edge **Ministral-3B** base model, it achieves state-of-the-art problem solving in mathematics, algorithmic coding, and multi-hop logical deduction.
 
-### 🔑 Key Features
-* 🧠 **DeepThink Chain-of-Thought (CoT)**: Always reasons step-by-step inside `<think>...</think>` tags before generating the final response.
-* 🛡️ **Level-0 Native Built-in Preset**: System prompt and calibrated sampling parameters are embedded at the tokenizer and architectural level. Works automatically in every client with zero configuration.
-* ⚡ **Ultra-Lightweight**: Only **2.2 GB - 4.0 GB VRAM required**, running smoothly on consumer GPUs, laptops, and single-board computers (SBCs).
-* 🌐 **14+ Platform Ready**: Comes with out-of-the-box configurations for Ollama, LM Studio, Jan, vLLM, Colab, Kaggle, Docker, and more.
+- **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]
 
-## 🧠 Level-0 Native System Prompt
+<!-- Provide the basic links for the model. -->
 
-The model contains an embedded, non-disableable system prompt:
-```text
-You are a thinking neural network and your name is Fwizzer-R1-3B-EN. Write your entire train of thought and steps inside the <think> (write first) and </think> (write at the end of reasoning) tags, and the final answer must be written after them. Always respond and reason strictly in the language the user addressed you in.
-```
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
-### Calibrated Sampling Parameters
-| Parameter | Recommended Value | Description |
-| :--- | :--- | :--- |
-| **Temperature** | `0.6` | Golden ratio for logical rigor and creativity |
-| **Top-P** | `0.95` | Ensures high-quality token probability sampling |
-| **Repeat Penalty** | `1.15` | Prevents reasoning loops |
-| **Context Length** | `8192` (up to `32768`) | Long-context comprehension window |
-| **Stop Tokens** | `</s>`, `[INST]`, `[/INST]` | Clean generation termination |
+## 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. -->
 
-## 📦 GGUF Quantization Matrix
+### Direct Use
 
-| Flavor | Quantization | Size | Required VRAM / RAM | Recommended Hardware |
-| :--- | :--- | :--- | :--- | :--- |
-| **Speed** | `Q4_K_M` | ~2.1 GB | ~2.5 GB | Laptops, integrated graphics, Raspberry Pi |
-| **Balanced** | `Q5_K_M` | ~2.5 GB | ~3.0 GB | **Recommended**: Best balance of quality & speed |
-| **Max** | `Q8_0` | ~3.6 GB | ~4.2 GB | Demanding code & complex mathematical research |
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
----
+[More Information Needed]
 
-## 🌐 14-Platform Universal Guide
-
-### 1. 📓 Google Colab
-Run on free Google Colab T4 GPU in one click:
-```python
-!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
-!pip install --no-deps "xformers<0.0.27" trl peft accelerate bitsandbytes
-
-from unsloth import FastLanguageModel
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "fwizzer1/Fwizzer-R1-3B-EN-v2",
-    max_seq_length = 8192,
-    load_in_4bit = True,
-)
-FastLanguageModel.for_inference(model)
-
-prompt = tokenizer.apply_chat_template(
-    [{"role": "user", "content": "Explain the Monty Hall problem step-by-step with Bayes Theorem."}],
-    tokenize = False,
-    add_generation_prompt = True
-)
-inputs = tokenizer([prompt], return_tensors = "pt").to("cuda")
-outputs = model.generate(**inputs, max_new_tokens = 1024, temperature = 0.6)
-print(tokenizer.decode(outputs[0], skip_special_tokens = False))
-```
-
-### 2. 🏆 Kaggle Notebooks
-Instant inference on Kaggle Tesla T4:
-```python
-from unsloth import FastLanguageModel
-model, tokenizer = FastLanguageModel.from_pretrained("fwizzer1/Fwizzer-R1-3B-EN-v2", load_in_4bit=True)
-```
-
-### 3. 🦙 llama.cpp & llama-server
-#### Interactive CLI:
-```bash
-llama-cli -m Fwizzer-R1-3B-EN-Balanced.gguf -p "[INST] Who are you? [/INST]" -c 8192 --temp 0.6 --top-p 0.95 --repeat-penalty 1.15
-```
-#### Production HTTP Server:
-```bash
-llama-server -m Fwizzer-R1-3B-EN-Balanced.gguf --jinja -c 8192 --port 8080 --host 0.0.0.0
-```
-
-### 4. 🖥️ LM Studio
-1. Load `Fwizzer-R1-3B-EN-Balanced.gguf`.
-2. Select the included preset `fwizzer-r1-3b-en-v2.preset.json`.
-3. LM Studio will automatically fold `<think>` tags with a smooth thinking animation!
-
-### 5. 📱 Jan AI
-Place `Fwizzer-R1-3B-EN-Balanced.gguf` and `jan-model.json` into `~/jan/models/fwizzer-r1-3b-en-v2`.
-
-### 6. ⚡ vLLM (High-Throughput Production)
-```bash
-vllm serve fwizzer1/Fwizzer-R1-3B-EN-v2     --port 8000     --max-model-len 8192     --gpu-memory-utilization 0.9
-```
-
-### 7. 🦙 Ollama
-Using the included `Modelfile`:
-```bash
-ollama create fwizzer-r1-3b-en-v2 -f ./Modelfile
-ollama run fwizzer-r1-3b-en-v2 "Write a Python script for quicksort with time complexity analysis."
-```
-
-### 8. 🦥 Unsloth Desktop
-Search for `fwizzer1/Fwizzer-R1-3B-EN-v2` directly in the UI.
-
-### 9. 🥧 Pi (Raspberry Pi 5 / SBCs)
-On Raspberry Pi 5 (8GB):
-```bash
-./llama-cli -m Fwizzer-R1-3B-EN-Speed.gguf -t 4 -c 4096 -p "[INST] Hello! [/INST]"
-```
-
-### 10. 🐳 Docker Model Runner
-```bash
-docker run --gpus all -v $(pwd):/models -p 8080:8080 ghcr.io/ggerganov/llama.cpp:server     -m /models/Fwizzer-R1-3B-EN-Balanced.gguf -c 8192 --host 0.0.0.0 --port 8080
-```
-
-### 11. 🍋 Lemonade
-Import the GGUF with default temperature `0.6`.
-
-### 12. 🤖 Hermes Agent
-Add to `agent.json`:
-```json
-{
-  "model": "fwizzer1/Fwizzer-R1-3B-EN-v2",
-  "temperature": 0.6,
-  "thinking_tokens": true
-}
-```
-
-### 13. ⚛️ Atomic Chat
-Set the API endpoint to `http://localhost:8080/v1`.
-
-### 14. 🦞 OpenClaw
-Set reasoning tag to `think` for deep thought inspection.
+### Downstream Use [optional]
 
----
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-## 📊 Benchmarks
+[More Information Needed]
 
-| Model | Parameters | GSM8K (Math) | MATH (Hard) | HumanEval (Code) | IFEval (Instructions) |
-| :--- | :--- | :--- | :--- | :--- | :--- |
-| **Fwizzer-R1-3B-EN-v2 (Ours)** | **3.29B** | **84.1%** | **56.3%** | **65.2%** | **73.4%** |
-| Ministral-3B Base | 3.29B | 61.2% | 32.4% | 48.1% | 58.6% |
-| Llama-3.2-3B-Instruct | 3.21B | 65.4% | 34.1% | 50.6% | 62.1% |
-| Qwen-2.5-3B-Instruct | 3.09B | 76.8% | 46.2% | 58.2% | 66.8% |
+### Out-of-Scope Use
 
----
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-## 📜 License
-Licensed under **Apache-2.0**. Free for research, personal, and commercial applications.
+[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]
+### Framework versions
 
-## 🖋️ Citation
-```bibtex
-@misc{fwizzer2026r1env2,
-  title={Fwizzer-R1-3B-EN-v2: Specialized English Reasoning Model},
-  author={Fwizzer Team},
-  year={2026},
-  publisher={Hugging Face},
-  howpublished={\url{https://huggingface.co/fwizzer1/Fwizzer-R1-3B-EN-v2}}
-}
-```
+- PEFT 0.19.1

en/adapter_model.safetensors

@@ -1,3 +1,3 @@
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