Update README.md

README.md

@@ -1,199 +1,54 @@
 ---
-library_name: transformers
-tags: []
+language:
+- en
+license: cc-by-4.0
+library_name: peft
+datasets:
+- Salesforce/xlam-function-calling-60k
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-<!-- 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. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-<!-- 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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+This is an adapter for [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) fine-tuned for function calling on xLAM. This adapter is undertrained. Its main purpose is for testing function calling capabilities of LLMs.
+
+```
+import torch, os
+from peft import PeftModel
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer
+)
+
+#use bf16 and FlashAttention if supported
+if torch.cuda.is_bf16_supported():
+  os.system('pip install flash_attn')
+  compute_dtype = torch.bfloat16
+  attn_implementation = 'flash_attention_2'
+else:
+  compute_dtype = torch.float16
+  attn_implementation = 'sdpa'
+
+adapter= "kaitchup/Meta-Llama-3-8B-xLAM-Adapter"
+model_name = "meta-llama/Meta-Llama-3-8B"
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=compute_dtype,
+    device_map={"": 0},
+    attn_implementation=attn_implementation,
+)
+
+model = PeftModel.from_pretrained(model, adapter)
+
+prompt = "<user>Check if the numbers 8 and 1233 are powers of two.</user>\n\n<tools>"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, do_sample=False, temperature=0.0, max_new_tokens=150)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+
+
+- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
+- **Language(s) (NLP):** English
+- **License:** cc-by-4.0