Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- HuggingFaceH4/Bespoke-Stratos-17k
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+Introducing Llama-3.2-1B-Instruct-Open-R1-Distill based on Llama-3.2-1B-Instruct and HuggingFace's [OpenR1](https://github.com/huggingface/open-r1) which is the fully open reproduction of DeepSeek-R1.
+
+I have always been passionate about advancing state-of-the-art LLM technology in smaller models that can run efficiently on laptop CPUs and smartphones.
+Thanks to the recent breakthrough of DeepSeek-R1, it has become surprisingly easy to develop a reasoning model through distillation,
+requiring only supervised fine-tuning (SFT) on a dataset generated by a teacher model.
+Additionally, with the tools provided by Hugging Face, we now have a streamlined method to achieve this.
+
+To reprdouce the results, simply go to HuggingFace's [OpenR1](https://github.com/huggingface/open-r1) and install the package.
+
+And then execute the following command:
+```
+ACCELERATE_LOG_LEVEL=info accelerate launch --config_file recipes/accelerate_configs/zero3.yaml src/open_r1/sft.py --config recipes/config_llama3_instrcut_1b.yaml
+```
+
+You can create your own ```recipes/config_llama3_instrcut_1b.yaml``` by copying (config_full.yaml)[https://github.com/huggingface/open-r1/blob/main/recipes/qwen/Qwen2.5-1.5B-Instruct/sft/config_full.yaml]
+to the desired folder and change model path to ```model_name_or_path: meta-llama/Llama-3.2-1B-Instruct``` or any HuggingFace model repo id you are interested in. 
+You may also choose to training for more than 1 epoch (I trained for 5 epoch).
+Also, if you want to get intermediate checkpoints, set the save parameters accordingly:
+
+```
+save_strategy: "steps"
+save_steps: 100
+```
+
+I have tried to use 1 for both train and eval batch size on 1 Nvidia 4090 but still got OOM so I rented 4 x LS40s from [vast.ai]. Training 5 epoch only required < 4 hours.
+```
+per_device_eval_batch_size: 4
+per_device_train_batch_size: 4
+```
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** keeeeenw
+- **Funded by [optional]:** myself for < $500
+- **Model type:** Llama-3.2-1B-Instruct with reasoning capability
+- **License:** Apache License 2.0
+- **Finetuned from model [optional]:** Llama-3.2-1B-Instruct
+
+## Uses
+
+```
+model = LlamaForCausalLM.from_pretrained("keeeeenw/Llama-3.2-1B-Instruct-Open-R1-Distill")
+
+# Prompt supported by HuggingFaceH4/Bespoke-Stratos-17k
+messages = [
+    {
+        "role": "system",
+        "content": "Your role as an assistant involves thoroughly exploring questions through a systematic long thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution. In the Thought section, detail your reasoning process using the specified format: <|begin_of_thought|> {thought with steps separated with '\n\n'} <|end_of_thought|> Each step should include detailed considerations such as analisying questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The solution should remain a logical, accurate, concise expression style and detail necessary step needed to reach the conclusion, formatted as follows: <|begin_of_solution|> {final formatted, precise, and clear solution} <|end_of_solution|> Now, try to solve the following question through the above guidelines:",
+    },
+    # question from https://www.reddit.com/r/LocalLLaMA/comments/13zz8y5/what_questions_do_you_ask_llms_to_check_their/
+    {"role": "user", "content": "Please provide me instructions on how to steal an egg from my chicken?"},
+ ]
+formatted_chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, return_tensors="pt")
+print(formatted_chat)
+
+inputs = tokenizer(formatted_chat, return_tensors="pt", padding=True)
+attention_mask = inputs["attention_mask"]
+
+streamer = TextStreamer(tokenizer, skip_prompt=True)
+outputs = model.generate(inputs['input_ids'],
+                         streamer=streamer,
+                         attention_mask=attention_mask,
+                         pad_token_id=tokenizer.eos_token_id,
+                         max_new_tokens=2048)
+print(tokenizer.decode(outputs[0]))
+```
+
+### 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]