Update README.md

README.md

@@ -1,201 +1,166 @@
 ---
-library_name: transformers
-tags: []
+dataset: Thermostatic/flowers
+license: other
+license_name: gemma-terms-of-use
+license_link: https://ai.google.dev/gemma/terms
 ---
 
-# Model Card for Model ID
+# Gemma Orchid 7b
 
-<!-- Provide a quick summary of what the model is/does. -->
+<div align="center">  
 
+![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/7pqiroePJW0WWm6JxwBoO.webp)
 
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+</div>
 
-## Model Details
+This model is the second checkpoint of a future project. Its capable of function calling as well as having a strong base in communicational skills.
 
-### Model Description
+This model has been finetuned on roughly 80k samples so far.
 
-<!-- Provide a longer summary of what this model is. -->
+# Training
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
++ Time to complete: ~20 hours
++ Datasets: Thermostatic/flowers, Intel/orca_dpo_pairs, jondurbin/truthy-dpo-v0.1
++ Cost: ~$20 in H100 hours
++ Evaluation loss: 0.69
++ Method: LoRa
++ Prompt Format: ChatML
 
-- **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]
+Thermostatic/flowers is a blend of open source model generations formatted in ShareGPT. It also includes all of capybara. 
 
-### Model Sources [optional]
+#### Running the model on a CPU
 
-<!-- Provide the basic links for the model. -->
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
 
-## Uses
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt")
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+#### Running the model on a single / multi GPU
 
-[More Information Needed]
 
-### Downstream Use [optional]
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto")
 
-[More Information Needed]
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 
-### Out-of-Scope Use
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+#### Running the model on a GPU using different precisions
 
-## Bias, Risks, and Limitations
+* _Using `torch.float16`_
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.float16)
 
-### Recommendations
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+* _Using `torch.bfloat16`_
 
-## How to Get Started with the Model
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-Use the code below to get started with the model.
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", device_map="auto", torch_dtype=torch.bfloat16)
 
-[More Information Needed]
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 
-## Training Details
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
-### Training Data
+#### Quantized Versions through `bitsandbytes`
 
-<!-- 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. -->
+* _Using 8-bit precision (int8)_
 
-[More Information Needed]
+```python
+# pip install bitsandbytes accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
 
-### Training Procedure 
+quantization_config = BitsAndBytesConfig(load_in_8bit=True)
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
 
-#### Preprocessing [optional]
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 
-[More Information Needed]
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
+* _Using 4-bit precision_
 
-#### Training Hyperparameters
+```python
+# pip install bitsandbytes accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+quantization_config = BitsAndBytesConfig(load_in_4bit=True)
 
-#### Speeds, Sizes, Times [optional]
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
+model = AutoModelForCausalLM.from_pretrained("google/gemma-7b", quantization_config=quantization_config)
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
 
-[More Information Needed]
+outputs = model.generate(**input_ids)
+print(tokenizer.decode(outputs[0]))
+```
 
-## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+#### Other optimizations
 
-### Testing Data, Factors & Metrics
+* _Flash Attention 2_
 
-#### Testing Data
+First make sure to install `flash-attn` in your environment `pip install flash-attn`
 
-<!-- This should link to a Dataset Card if possible. -->
+```diff
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
++   attn_implementation="flash_attention_2"
+).to(0)
+```
 
-[More Information Needed]
+### Inputs and outputs
 
-#### Factors
+*   **Input:** Text string, such as a question, a prompt, or a document to be
+    summarized.
+*   **Output:** Generated English-language text in response to the input, such
+    as an answer to a question, or a summary of a document.
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+## Evaluations 
 
-[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]
+In progress
 
+## GGUF + iMatrix
 
+In progress