Improve language tag

README.md

@@ -1,205 +1,217 @@
----
-license: apache-2.0
-datasets:
-- amphora/QwQ-LongCoT-130K-2
-- PowerInfer/QWQ-LONGCOT-500K
-- PowerInfer/LONGCOT-Refine-500K
-language:
-- en
-metrics:
-- perplexity
-base_model:
-- Qwen/Qwen2.5-0.5B-Instruct
-library_name: transformers
----
-## Model Details:
-
-- **Base Model:** Qwen/Qwen2-0.5B-Instruct  
-- **Teacher Model:** Qwen/QwQ-32B-Preview  
-- **Distillation Framework:** Instruction Tuning  
-- **Task Type:** Conversational AI / Causal Language Modeling  
-- **Parameters:** 0.5B  
-- **Special Features:**
-  - Integrated gradient checkpointing for efficient training
-  - Step-by-step reasoning capabilities for better problem-solving
-
----
-
-## Training:
-
-QwQ-0.5B-Distilled was trained using the **QwQ-LongCoT-130K dataset**, a carefully curated collection of long-context examples designed for reasoning and conversational AI tasks. The GKD framework ensures that the student model mimics the teacher model’s outputs, aligning its predictions with high-quality responses.
-### Training Progress:
-[▓▓▓▓▓▓▓▓▓▓] 100%
-
-### Training Script:
-
-```python
-import os
-import argparse
-import torch
-from datasets import Dataset
-from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-)
-from datasets import load_dataset
-from peft import LoraConfig
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--max_length", type=int, default = 4096)
-parser.add_argument("--output_dir", type=str, default="gkd-model")
-parser.add_argument("--per_device_train_batch_size", type=int, default=1)
-parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
-parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
-parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
-parser.add_argument("--lora", action="store_true")
-args = parser.parse_args()
-
-qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
-messages = []
-for each in qwq_dataset:
-    msg = [
-        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
-        {"role": "user", "content": each["problem"]},
-        {"role": "assistant", "content": each["qwq"]},
-    ]
-    messages.append(msg)
-
-TRAIN_SPLIT_RATIO = 0.9
-train_size = int(TRAIN_SPLIT_RATIO * len(messages))
-eval_size = len(messages) - train_size
-
-tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
-
-# The model to optimise
-model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 
-
-
-
-### Real Dataset
-train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
-eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
-training_args = SFTConfig(
-    output_dir=args.output_dir,
-    max_seq_length=args.max_length,
-    per_device_train_batch_size=args.per_device_train_batch_size,
-    gradient_accumulation_steps=args.gradient_accumulation_steps,
-    gradient_checkpointing = args.gradient_checkpointing,
-    save_steps = 100,
-    save_total_limit = 5
-    )
-
-lora_config = LoraConfig(
-    r=16,
-    lora_alpha=32,
-    lora_dropout=0.05,
-    bias="none",
-    task_type="CAUSAL_LM",
-)
-
-response_template = "<|im_start|>assistant\n"
-
-collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)
-
-trainer = SFTTrainer(
-    model=model,
-    args=training_args,
-    processing_class=tokenizer,
-    train_dataset=train_dataset,
-    eval_dataset=eval_dataset,
-    peft_config=lora_config if args.lora else None,
-    data_collator=collator,
-)
-trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)
-```
-
-### Dataset:  
-- **Source:** `amphora/QwQ-LongCoT-130K`  
-- **Split:** 90% Training, 10% Evaluation  
-
----
-
-## Example Usage:
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-# Model name
-model_name = "kz919/QwQ-0.5B-Distilled-SFT"
-# Load the model
-print(f"Starting to load the model {model_name} into memory")
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype=torch.bfloat16,
-    device_map={"": 0}
-)
-# Load the tokenizer
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-# Define the prompt
-prompt = "How many r in strawberry."
-messages = [
-    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
-    {"role": "user", "content": prompt}
-]
-# Tokenize the input
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-# Generate a response
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=4096
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-# Decode the response
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-print(response)
-```
-
----
-
-## Applications:
-
-1. **Conversational Assistants:**  
-   Suitable for AI chatbots that require reasoning and long-context understanding.  
-
-2. **Educational Tools:**  
-   Provides step-by-step explanations, making it ideal for learning environments.  
-
-3. **Creative Writing:**  
-   Assists in generating coherent, contextually aware long-form content.  
-
-4. **Technical Support:**  
-   Handles complex customer queries with precision and clarity.  
-
----
-
-## Limitations:
-
-- While distilled for efficiency, performance on highly complex reasoning tasks may slightly trail the teacher model.  
-- This model could still be under trained, merely a proof of concept. Don't yell at me if it's outputing nonesense.
----
-
-## Citation:
-
-If you use this model in your research or applications, please cite it as:
-
-```bibtex
-@model{qwq_0.5B_distilled,
-  author = {Kaizhao Liang},
-  title = {Mini-QwQ: A Reasoning Model for Edge Devices},
-  year = {2024},
-  publisher = {Hugging Face},
-  version = {1.0}
-}
-```
-
----
-
+---
+license: apache-2.0
+datasets:
+- amphora/QwQ-LongCoT-130K-2
+- PowerInfer/QWQ-LONGCOT-500K
+- PowerInfer/LONGCOT-Refine-500K
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+metrics:
+- perplexity
+base_model:
+- Qwen/Qwen2.5-0.5B-Instruct
+library_name: transformers
+---
+## Model Details:
+
+- **Base Model:** Qwen/Qwen2-0.5B-Instruct  
+- **Teacher Model:** Qwen/QwQ-32B-Preview  
+- **Distillation Framework:** Instruction Tuning  
+- **Task Type:** Conversational AI / Causal Language Modeling  
+- **Parameters:** 0.5B  
+- **Special Features:**
+  - Integrated gradient checkpointing for efficient training
+  - Step-by-step reasoning capabilities for better problem-solving
+
+---
+
+## Training:
+
+QwQ-0.5B-Distilled was trained using the **QwQ-LongCoT-130K dataset**, a carefully curated collection of long-context examples designed for reasoning and conversational AI tasks. The GKD framework ensures that the student model mimics the teacher model’s outputs, aligning its predictions with high-quality responses.
+### Training Progress:
+[▓▓▓▓▓▓▓▓▓▓] 100%
+
+### Training Script:
+
+```python
+import os
+import argparse
+import torch
+from datasets import Dataset
+from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+)
+from datasets import load_dataset
+from peft import LoraConfig
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--max_length", type=int, default = 4096)
+parser.add_argument("--output_dir", type=str, default="gkd-model")
+parser.add_argument("--per_device_train_batch_size", type=int, default=1)
+parser.add_argument("--gradient_accumulation_steps", type=int, default=16)
+parser.add_argument("--gradient_checkpointing", action="store_true", default=False)
+parser.add_argument("--resume_from_checkpoint", action="store_true", default=False)
+parser.add_argument("--lora", action="store_true")
+args = parser.parse_args()
+
+qwq_dataset = load_dataset("amphora/QwQ-LongCoT-130K-2", split = "train")
+messages = []
+for each in qwq_dataset:
+    msg = [
+        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+        {"role": "user", "content": each["problem"]},
+        {"role": "assistant", "content": each["qwq"]},
+    ]
+    messages.append(msg)
+
+TRAIN_SPLIT_RATIO = 0.9
+train_size = int(TRAIN_SPLIT_RATIO * len(messages))
+eval_size = len(messages) - train_size
+
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+
+# The model to optimise
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", torch_dtype=torch.bfloat16, device_map="auto") 
+
+
+
+### Real Dataset
+train_dataset = Dataset.from_dict({"messages":messages[:train_size]})
+eval_dataset = Dataset.from_dict({"messages":messages[train_size:]})
+training_args = SFTConfig(
+    output_dir=args.output_dir,
+    max_seq_length=args.max_length,
+    per_device_train_batch_size=args.per_device_train_batch_size,
+    gradient_accumulation_steps=args.gradient_accumulation_steps,
+    gradient_checkpointing = args.gradient_checkpointing,
+    save_steps = 100,
+    save_total_limit = 5
+    )
+
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+)
+
+response_template = "<|im_start|>assistant\n"
+
+collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)
+
+trainer = SFTTrainer(
+    model=model,
+    args=training_args,
+    processing_class=tokenizer,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    peft_config=lora_config if args.lora else None,
+    data_collator=collator,
+)
+trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)
+```
+
+### Dataset:  
+- **Source:** `amphora/QwQ-LongCoT-130K`  
+- **Split:** 90% Training, 10% Evaluation  
+
+---
+
+## Example Usage:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Model name
+model_name = "kz919/QwQ-0.5B-Distilled-SFT"
+# Load the model
+print(f"Starting to load the model {model_name} into memory")
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map={"": 0}
+)
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Define the prompt
+prompt = "How many r in strawberry."
+messages = [
+    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+    {"role": "user", "content": prompt}
+]
+# Tokenize the input
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+# Generate a response
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=4096
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+# Decode the response
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+---
+
+## Applications:
+
+1. **Conversational Assistants:**  
+   Suitable for AI chatbots that require reasoning and long-context understanding.  
+
+2. **Educational Tools:**  
+   Provides step-by-step explanations, making it ideal for learning environments.  
+
+3. **Creative Writing:**  
+   Assists in generating coherent, contextually aware long-form content.  
+
+4. **Technical Support:**  
+   Handles complex customer queries with precision and clarity.  
+
+---
+
+## Limitations:
+
+- While distilled for efficiency, performance on highly complex reasoning tasks may slightly trail the teacher model.  
+- This model could still be under trained, merely a proof of concept. Don't yell at me if it's outputing nonesense.
+---
+
+## Citation:
+
+If you use this model in your research or applications, please cite it as:
+
+```bibtex
+@model{qwq_0.5B_distilled,
+  author = {Kaizhao Liang},
+  title = {Mini-QwQ: A Reasoning Model for Edge Devices},
+  year = {2024},
+  publisher = {Hugging Face},
+  version = {1.0}
+}
+```
+
+---
+
 This model is an example of how efficient fine-tuning and distillation methods can deliver robust conversational AI capabilities in a smaller, more manageable footprint.