Improve language tag

README.md

@@ -1,73 +1,85 @@
----
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-library_name: transformers
----
-## Introduction
-FLock Web3 Agent Model is a specialized LLM designed to address complex queries in the Web3 ecosystem, with a focus on DeFi, blockchain interoperability, on-chain analytics, and etc.. The model excels in function-calling reasoning, enabling it to break down intricate user requests into actionable steps, interact with external APIs, and provide data-driven insights for Web3 applications. It is tailored for users ranging from developers and researchers to investors navigating the decentralized landscape.
-## Requirements
-We advise you to use the latest version of `transformers`.
-
-## Quickstart
-
-Given a query and a list of available tools. The model generate function calls using the provided tools to respond the query correctly.
-
-**Example query and tools format**
-
-```python
-input_example=
-{
-  "query": "Track crosschain message verification, implement timeout recovery procedures.",
-  "tools": [
-    {"type": "function", "function": {"name": "track_crosschain_message", "description": "Track the status of a crosschain message", "parameters": {"type": "object", "properties": {"message_id": {"type": "string"}}}}},
-    {"type": "function", "function": {"name": "schedule_timeout_check", "description": "Schedule a timeout check for a message", "parameters": {"type": "object", "properties": {"message_id": {"type": "string"}, "timeout": {"type": "integer"}}}}}
-  ]
-}
-
-```
-
-**Function calling generation**
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import json
-
-model_name = "flock-io/Flock_Web3_Agent_Model"
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-messages = [
-    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required -"
-                                   + json.dumps(input_example["tools"], ensure_ascii=False)},
-    {"role": "user", "content": input_example["query"]}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=3000
-)
-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]
-```
-
-The output text is in the string format
-
-```
-[
-    {"name": "track_crosschain_message", "arguments": {"message_id": "msg12345"}},
-    {"name": "schedule_timeout_check", "arguments": {"message_id": "msg12345", "timeout": "30"}}
-  ]
+---
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+library_name: transformers
+---
+## Introduction
+FLock Web3 Agent Model is a specialized LLM designed to address complex queries in the Web3 ecosystem, with a focus on DeFi, blockchain interoperability, on-chain analytics, and etc.. The model excels in function-calling reasoning, enabling it to break down intricate user requests into actionable steps, interact with external APIs, and provide data-driven insights for Web3 applications. It is tailored for users ranging from developers and researchers to investors navigating the decentralized landscape.
+## Requirements
+We advise you to use the latest version of `transformers`.
+
+## Quickstart
+
+Given a query and a list of available tools. The model generate function calls using the provided tools to respond the query correctly.
+
+**Example query and tools format**
+
+```python
+input_example=
+{
+  "query": "Track crosschain message verification, implement timeout recovery procedures.",
+  "tools": [
+    {"type": "function", "function": {"name": "track_crosschain_message", "description": "Track the status of a crosschain message", "parameters": {"type": "object", "properties": {"message_id": {"type": "string"}}}}},
+    {"type": "function", "function": {"name": "schedule_timeout_check", "description": "Schedule a timeout check for a message", "parameters": {"type": "object", "properties": {"message_id": {"type": "string"}, "timeout": {"type": "integer"}}}}}
+  ]
+}
+
+```
+
+**Function calling generation**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import json
+
+model_name = "flock-io/Flock_Web3_Agent_Model"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required -"
+                                   + json.dumps(input_example["tools"], ensure_ascii=False)},
+    {"role": "user", "content": input_example["query"]}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=3000
+)
+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]
+```
+
+The output text is in the string format
+
+```
+[
+    {"name": "track_crosschain_message", "arguments": {"message_id": "msg12345"}},
+    {"name": "schedule_timeout_check", "arguments": {"message_id": "msg12345", "timeout": "30"}}
+  ]
 ```