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README.md

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+### exl2 quant (measurement.json included)
+---
+### original readme below
+---
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+model-index:
+- name: Rubra-Qwen2-7B-Instruct
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: MMLU
+      name: MMLU
+    metrics:
+    - type: 5-shot
+      value: 68.88
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: GPQA
+      name: GPQA
+    metrics:
+    - type: 0-shot
+      value: 30.36
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: GSM-8K
+      name: GSM-8K
+    metrics:
+    - type: 8-shot, CoT
+      value: 75.82
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: MATH
+      name: MATH
+    metrics:
+    - type: 4-shot, CoT
+      value: 28.72
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: MT-bench
+      name: MT-bench
+    metrics:
+    - type: GPT-4 as Judge
+      value: 8.08
+      verified: false
+tags:
+- function-calling
+- tool-calling
+- agentic
+- rubra
+- conversational
+language:
+- en
+- zh
+---
+
+# Qwen2 7B Instruct
+
+## Model description
+The model is the result of further post-training [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct). It is capable of complex multi-turn tool/function calling.
+
+## Training
+
+The model was post-trained (freeze tuned & DPO) on a proprietary dataset consisting of diverse function calling, chat, and instruct data.
+
+## How to use
+
+You can use the model with the Hugging Face `transformers` and the rubra library [rubra-tools](https://github.com/rubra-ai/rubra-tools) as follows:
+
+```
+pip install rubra_tools torch==2.3.0 transformers accelerate
+```
+
+You also need Node.js and npm installed. Once you do, install the `jsonrepair` package - it's used to fix some rare hallucinations by the model.
+
+```
+npm install jsonrepair
+```
+
+### 1. Load the Model
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+from rubra_tools import preprocess_input, postprocess_output
+
+model_id = "rubra-ai/Qwen2-7B-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    device_map="auto",
+)
+```
+
+### 2. Define Functions
+
+Here we use 4 functions for a simple math chaining question:
+```python
+functions = [
+    {
+            'type': 'function',
+            'function': {
+                'name': 'addition',
+                'description': "Adds two numbers together",
+                'parameters': {
+                    'type': 'object',
+                    'properties': {
+                        'a': {
+                            'description': 'First number to add',
+                            'type': 'string'
+                        },
+                        'b': {
+                            'description': 'Second number to add',
+                            'type': 'string'
+                        }
+                    },
+                    'required': []
+                }
+            }
+        },
+        {
+            'type': 'function',
+            'function': {
+                'name': 'subtraction',
+                'description': "Subtracts two numbers",
+                'parameters': {
+                    'type': 'object',
+                    'properties': {
+                        'a': {
+                            'description': 'First number to be subtracted from',
+                            'type': 'string'
+                        },
+                        'b': {
+                            'description': 'Number to subtract',
+                            'type': 'string'
+                        }
+                    },
+                    'required': []
+                }
+            }
+        },
+        {
+            'type': 'function',
+            'function': {
+                'name': 'multiplication',
+                'description': "Multiply two numbers together",
+                'parameters': {
+                    'type': 'object',
+                    'properties': {
+                        'a': {
+                            'description': 'First number to multiply',
+                            'type': 'string'
+                        },
+                        'b': {
+                            'description': 'Second number to multiply',
+                            'type': 'string'
+                        }
+                    },
+                    'required': []
+                }
+            }
+        },
+        {
+            'type': 'function',
+            'function': {
+                'name': 'division',
+                'description': "Divide two numbers",
+                'parameters': {
+                    'type': 'object',
+                    'properties': {
+                        'a': {
+                            'description': 'First number to use as the dividend',
+                            'type': 'string'
+                        },
+                        'b': {
+                            'description': 'Second number to use as the divisor',
+                            'type': 'string'
+                        }
+                    },
+                    'required': []
+                }
+            }
+        },
+]
+```
+
+### 3. Start the conversation
+```python
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "What is the result of four plus six? Take the result and add 2? Then multiply by 5 and then divide by two"},
+]
+
+def run_model(messages, functions):
+    ## Format messages in Rubra's format
+    formatted_msgs = preprocess_input(msgs=messages, tools=functions)
+
+    text = tokenizer.apply_chat_template(
+        formatted_msgs,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+    generated_ids = model.generate(
+        model_inputs.input_ids,
+        max_new_tokens=512
+    )
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return response
+
+
+raw_output = run_model(messages, functions)
+# Check if there's a function call
+function_call = postprocess_output(raw_output)
+if function_call:
+    print(function_call)
+else:
+    print(raw_output)
+```
+
+You should see this output, which is a function call made by the AI assistant:
+```
+[{'id': 'fc65a533', 'function': {'name': 'addition', 'arguments': '{"a": "4", "b": "6"}'}, 'type': 'function'}]
+```
+
+### 4. Add Executed Tool Result to Message History & Continue the Conversation
+
+```python
+if function_call:
+    # append the assistant tool call msg
+    messages.append({"role": "assistant", "tool_calls": function_call})
+    # append the result of the tool call in openai format, in this case, the value of add 6 to 4 is 10.
+    messages.append({'role': 'tool', 'tool_call_id': function_call[0]["id"], 'name': function_call[0]["function"]["name"], 'content': '10'})
+    raw_output1 = run_model(messages, functions)
+    # Check if there's a function call
+
+    function_call = postprocess_output(raw_output1)
+    if function_call:
+        print(function_call)
+    else:
+        print(raw_output)
+```
+
+The LLM will make another call
+```
+[{'id': '2ffc3de4', 'function': {'name': 'addition', 'arguments': '{"a": "10", "b": "2"}'}, 'type': 'function'}]
+```
+
+## Framework Versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1
+
+## Limitations and Bias
+
+While the model performs well on a wide range of tasks, it may still produce biased or incorrect outputs. Users should exercise caution and critical judgment when using the model in sensitive or high-stakes applications. The model's outputs are influenced by the data it was trained on, which may contain inherent biases.
+
+## Ethical Considerations
+
+Users should ensure that the deployment of this model adheres to ethical guidelines and consider the potential societal impact of the generated text. Misuse of the model for generating harmful or misleading content is strongly discouraged.
+
+## Acknowledgements
+
+We would like to thank Alibaba Cloud for the model.
+
+## Contact Information
+
+For questions or comments about the model, please reach out to [the rubra team](mailto:rubra@acorn.io).
+
+## Citation
+
+If you use this work, please cite it as:
+
+```
+@misc {rubra_ai_2024,
+	author       = { Sanjay Nadhavajhala and Yingbei Tong },
+	title        = { Rubra-Qwen2-7B-Instruct },
+	year         = 2024,
+	url          = { https://huggingface.co/rubra-ai/Qwen2-7B-Instruct },
+	doi          = { 10.57967/hf/2683 },
+	publisher    = { Hugging Face }
+}
+```

added_tokens.json

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+{
+  "<|endoftext|>": 151643,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644
+}

all_results.json

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+{
+    "epoch": 0.9999891664680519,
+    "total_flos": 6.312971135569623e+19,
+    "train_loss": 0.2639624638028189,
+    "train_runtime": 248662.3087,
+    "train_samples_per_second": 4.455,
+    "train_steps_per_second": 0.223
+}

config.json

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+{
+    "_name_or_path": "Qwen2-7B-Instruct/base_expanded",
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 3584,
+    "initializer_range": 0.02,
+    "intermediate_size": 18944,
+    "max_position_embeddings": 32768,
+    "max_window_layers": 28,
+    "model_type": "qwen2",
+    "num_attention_heads": 28,
+    "num_hidden_layers": 32,
+    "num_key_value_heads": 4,
+    "rms_norm_eps": 1e-06,
+    "rope_theta": 1000000.0,
+    "sliding_window": 131072,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.41.2",
+    "use_cache": false,
+    "use_sliding_window": false,
+    "vocab_size": 152064,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.1.6",
+        "bits": 6.0,
+        "head_bits": 6,
+        "calibration": {
+            "rows": 115,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "transformers_version": "4.41.2",
+  "use_cache": false
+}

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special_tokens_map.json

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+{
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+    "<|im_start|>",
+    "<|im_end|>"
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+}

tokenizer_config.json

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train_results.json

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+}