pankajmathur
/

orca_mini_v8_0_70b

@@ -8,196 +8,110 @@ base_model:
 - meta-llama/Llama-3.3-70B-Instruct
 library_name: transformers
 ---
-# Model Card for Model ID
-orca_mini_v8_0_Llama-3.3-70B-Instruct
-## Model Details
-pankajmathur/orca_mini_v8_0_70b
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **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]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## 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. -->
-### 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]

 - meta-llama/Llama-3.3-70B-Instruct
 library_name: transformers
 ---
+# Model Name: orca_mini_v8_0_Llama-3.3-70B-Instruct
+**orca_mini_v8_0_Llama-3.3-70B-Instruct is trained with various SFT Datasets**
+<img src="https://huggingface.co/pankajmathur/orca_mini_v5_8b/resolve/main/orca_minis_small.jpeg" width="auto" />
+<strong>
+Passionate about Generative AI? I help companies to privately train and deploy custom use case specific LLM/MLLM affordably. For startups, I can even assist with securing GPU grants to get you started. Let's chat!
+<a href="https://www.linkedin.com/in/pankajam" target="_blank">https://www.linkedin.com/in/pankajam</a> Looking forward to connecting!
+</strong>
+<br>
+### NOTICE
+By providing proper credit and attribution, you are granted permission to use this model as a foundational base for further Full fine tuning, DPO, PPO or ORPO tuning and any kind of Merges.
+I actively encourage users to customize and enhance the model according to their specific needs, as this version is designed to be a comprehensive general model.
+Dive in and innovate!
+### Example Usage
+Here is the Llama3 prompt format
+```
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+You are Orca Mini, a helpful AI assistant.<|eot_id|>
+<|start_header_id|>user<|end_header_id|>
+Hello Orca Mini, what can you do for me?<|eot_id|>
+<|start_header_id|>assistant<|end_header_id|>
+```
+Below shows a code example on how to use this model in default(bf16) format
+```python
+from transformers import AutoModel, AutoTokenizer
+model_slug = "pankajmathur/orca_mini_v8_0_70b"
+model = AutoModel.from_pretrained(model_slug)
+tokenizer = AutoTokenizer.from_pretrained(model_slug)
+messages = [
+    {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
+    {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
+]
+gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
+model.generate(**gen_input)
+```
+Below shows a code example on how to use this model in 4-bit format via bitsandbytes library
+```python
+from transformers import AutoModel, AutoTokenizer, BitsAndBytesConfig
+model_slug = "pankajmathur/orca_mini_v8_0_70b"
+quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+quantized_model = AutoModelForCausalLM.from_pretrained(
+	model_slug, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
+tokenizer = AutoTokenizer.from_pretrained(model_slug)
+messages = [
+    {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
+    {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
+]
+gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
+quantized_model.generate(**gen_input)
+```
+  Below shows a code example on how to do a tool use with this model and tranformer library, Since **orca_mini_v8_0_70b** based upon LLaMA-3.3 so it supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).
+Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers.
+Here is a quick example showing a single simple tool:
+```python
+# First, define a tool
+def get_current_temperature(location: str) -> float:
+    """
+    Get the current temperature at a location.
+    Args:
+        location: The location to get the temperature for, in the format "City, Country"
+    Returns:
+        The current temperature at the specified location in the specified units, as a float.
+    """
+    return 22.  # A real function should probably actually get the temperature!
+# Next, create a chat and apply the chat template
+messages = [
+  {"role": "system", "content": "You are a bot that responds to weather queries."},
+  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
+]
+inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
+```
+You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
+```python
+tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
+messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
+```
+and then call the tool and append the result, with the `tool` role, like so:
+```python
+messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
+```
+After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information,
+see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).