Improve language tag (#2)

- Improve language tag (0ab6c984db31f6cd3b80530e784c03e2de9f6d15)


Co-authored-by: Loïck BOURDOIS <[email protected]>

README.md

@@ -1,67 +1,79 @@
----
-base_model: Qwen/Qwen2.5-0.5B-Instruct
-language:
-- en
-license: apache-2.0
-datasets: 
-- KingNish/reasoning-base-20k
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen2
-- trl
-- sft
-- reasoning
----
-
-
-# Model Dexcription
-
-It's First iteration of this model. For testing purpose its just trained on 10k rows.
-It performed very well than expected. It do first reasoning and than generate response on based on it but it do like o1.
-It do reasoning separately no special tokens or in response reasoning.
-Below is inference code.
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-MAX_REASONING_TOKENS = 1024
-MAX_RESPONSE_TOKENS = 512
-
-model_name = "KingNish/Reasoning-0.5b"
-
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Which is greater 9.9 or 9.11 ??"
-messages = [
-    {"role": "user", "content": prompt}
-]
-
-# Generate reasoning
-reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
-reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
-reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
-reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
-
-# print("REASONING: " + reasoning_output)
-
-# Generate answer
-messages.append({"role": "reasoning", "content": reasoning_output})
-response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
-response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
-response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
-
-print("ANSWER: " + response_output)
-```
-
-- **Trained by:** [Nishith Jain](https://huggingface.co/KingNish)
-- **License:** apache-2.0
-- **Finetuned from model :** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
-- **Dataset used :** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
-
-
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-
+---
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+license: apache-2.0
+datasets:
+- KingNish/reasoning-base-20k
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- trl
+- sft
+- reasoning
+---
+
+
+# Model Dexcription
+
+It's First iteration of this model. For testing purpose its just trained on 10k rows.
+It performed very well than expected. It do first reasoning and than generate response on based on it but it do like o1.
+It do reasoning separately no special tokens or in response reasoning.
+Below is inference code.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MAX_REASONING_TOKENS = 1024
+MAX_RESPONSE_TOKENS = 512
+
+model_name = "KingNish/Reasoning-0.5b"
+
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Which is greater 9.9 or 9.11 ??"
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+# Generate reasoning
+reasoning_template = tokenizer.apply_chat_template(messages, tokenize=False, add_reasoning_prompt=True)
+reasoning_inputs = tokenizer(reasoning_template, return_tensors="pt").to(model.device)
+reasoning_ids = model.generate(**reasoning_inputs, max_new_tokens=MAX_REASONING_TOKENS)
+reasoning_output = tokenizer.decode(reasoning_ids[0, reasoning_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+# print("REASONING: " + reasoning_output)
+
+# Generate answer
+messages.append({"role": "reasoning", "content": reasoning_output})
+response_template = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+response_inputs = tokenizer(response_template, return_tensors="pt").to(model.device)
+response_ids = model.generate(**response_inputs, max_new_tokens=MAX_RESPONSE_TOKENS)
+response_output = tokenizer.decode(response_ids[0, response_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+
+print("ANSWER: " + response_output)
+```
+
+- **Trained by:** [Nishith Jain](https://huggingface.co/KingNish)
+- **License:** apache-2.0
+- **Finetuned from model :** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+- **Dataset used :** [KingNish/reasoning-base-20k](https://huggingface.co/datasets/KingNish/reasoning-base-20k)
+
+
+This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)