init

README.md

@@ -9,4 +9,110 @@ pipeline_tag: text-generation
 tags:
 - function-call
 - Qwen2
----
+---
+
+# Qwen2-7B-Instruct-glaive-function-calling
+
+## Introduction
+基于数据集 glaive-function-calling-v2 在 Qwen2-7B-Instruct 上进行微调而来
+<br>
+
+## Training details
+使用 lora 进行训练
+训练样本如下：
+```
+<|im_start|>system
+You are a helpful assistant with access to the following functions. Use them if required -
+{
+    "name": "generate_invoice",
+    "description": "Generate an invoice with specified details",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "customer_name": {
+                "type": "string",
+                "description": "The name of the customer"
+            },
+            "items": {
+                "type": "array",
+                "items": {
+                    "type": "object",
+                    "properties": {
+                        "name": {
+                            "type": "string",
+                            "description": "The name of the item"
+                        },
+                        "quantity": {
+                            "type": "integer",
+                            "description": "The quantity of the item"
+                        },
+                        "price": {
+                            "type": "number",
+                            "description": "The price of the item"
+                        }
+                    },
+                    "required": [
+                        "name",
+                        "quantity",
+                        "price"
+                    ]
+                }
+            }
+        },
+        "required": [
+            "customer_name",
+            "items"
+        ]
+    }
+}
+<|im_end|>
+<|im_start|>user
+I need to generate an invoice for a customer named John Doe. He bought 2 apples for $1 each and 3 oranges for $2 each.<|im_end|>
+<|im_start|>assistant
+<functioncall> {"name": "generate_invoice", "arguments": '{"customer_name": "John Doe", "items": [{"name": "apple", "quantity": 2, "price": 1}, {"name": "orange", "quantity": 3, "price": 2}]}'} <|endoftext|><|im_end|>
+<|im_start|>function
+{"invoice_id": "INV12345", "customer_name": "John Doe", "items": [{"name": "apple", "quantity": 2, "price": 1, "total": 2}, {"name": "orange", "quantity": 3, "price": 2, "total": 6}], "total": 8, "status": "Generated"}<|im_end|>
+<|im_start|>assistant
+The invoice has been successfully generated. The invoice ID is INV12345. The total amount for 2 apples and 3 oranges is $8. <|endoftext|><|im_end|>
+```
+
+
+## Quickstart
+
+> 参考 Qwen2-7B-Instruct
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2-7B-Instruct",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(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]
+```

added_tokens.json

@@ -0,0 +1,5 @@
+{
+  "<|endoftext|>": 151643,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644
+}

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "Qwen2-7B-Instruct",
+  "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": 28,
+  "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": "4.38.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.38.1"
+}

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special_tokens_map.json

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tokenizer_config.json

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