Update README.md

README.md

@@ -1,199 +1,96 @@
 ---
 library_name: transformers
-tags: []
+datasets:
+- hypervariance/function-calling-sharegpt
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+Gemma 2B function calling. [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) finetuned on [hypervariance/function-calling-sharegpt](https://huggingface.co/datasets/hypervariance/function-calling-sharegpt).
 
 
+## Usage
 
-## Model Details
+```python
+from transformers import AutoModelForCausalLM , AutoTokenizer
 
-### Model Description
+tokenizer = AutoTokenizer.from_pretrained("rodrigo-pedro/gemma-2b-function-calling", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("rodrigo-pedro/gemma-2b-function-calling", trust_remote_code=True, device_map="auto")
 
-<!-- Provide a longer summary of what this model is. -->
+inputs = tokenizer(prompt,return_tensors="pt").to(model.device)
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+outputs = model.generate(**inputs,do_sample=True,temperature=0.1,top_p=0.95,max_new_tokens=100)
 
-- **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]
+print(tokenizer.decode(outputs[0]))
+```
 
-### Model Sources [optional]
+You can also use sharegpt formatted prompts:
 
-<!-- Provide the basic links for the model. -->
+```python
+from transformers import AutoModelForCausalLM , AutoTokenizer
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained("rodrigo-pedro/gemma-2b-function-calling", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("rodrigo-pedro/gemma-2b-function-calling", trust_remote_code=True, device_map="auto")
 
-## Uses
+chat = [
+  {
+      "from": "system",
+      "value": "SYSTEM PROMPT",
+  },
+  {
+      "from": "human",
+      "value": "USER QUESTION"
+  },
+]
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
 
-### Direct Use
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+outputs = model.generate(**inputs,do_sample=True,temperature=0.1,top_p=0.95,max_new_tokens=100)
 
-[More Information Needed]
+print(tokenizer.decode(outputs[0]))
+```
 
-### Downstream Use [optional]
+## Prompt template
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+```text
+You are a helpful assistant with access to the following functions. Use them if required -
+{
+    "name": "function name",
+    "description": "function description",
+    "parameters": {
+        "type": "type (object/number/string)",
+        "properties": {
+            "property_1": {
+                "type": "type",
+                "description": "property description"
+            }
+        },
+        "required": [
+            "property_1"
+        ]
+    }
+}
 
-[More Information Needed]
+To use these functions respond with:
+<functioncall> {"name": "function_name", "arguments": {"arg_1": "value_1", "arg_1": "value_1", ...}} </functioncall>
 
-### Out-of-Scope Use
+Edge cases you must handle:
+ - If there are no functions that match the user request, you will respond politely that you cannot help.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+User Question:
+USER_QUESTION
+```
 
-[More Information Needed]
+Function calls are enclosed in `<functioncall>` `</functioncall>`.
 
-## Bias, Risks, and Limitations
+The model was trained using the same delimiters as [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it):
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+```text
+<bos><start_of_turn>user
+Write a hello world program<end_of_turn>
+<start_of_turn>model
+```
 
-[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]
+Use `<end_of_turn>` stop sequence to prevent the model from generating further text.