Update model card for Bashkir

README.md

@@ -1,86 +1,89 @@
----
-language: bak
-license: apache-2.0
-tags:
-  - trimmed
-  - qwen3.5
-base_model: Qwen/Qwen3.5-2B
-base_model_relation: quantized
-datasets:
-  - Lumberjackk/fineweb-2-trimming
----
-
-# Qwen3.5-2B-bak-32768
-
-This model is a **19.95% smaller** version of [Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B) optimized for Bashkir language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/introduction-to-trimming) method.  
-
-This trimmed model should perform similarly to the original model with only **32,768 tokens** and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in Bashkir were removed from the vocabulary.
-
-Note: Qwen3.5 is a multimodal (vision-language) model. This trimmed version retains the vision encoder but reduces only the text vocabulary.
-
-## Model Statistics
-
-| Metric | Original | Trimmed | Reduction |
-|--------|----------|---------|-----------|
-| **Vocabulary size** | 248,044 tokens | 32,768 tokens | **86.79%** |
-| **Model size** | 2,213,241,664 params | 1,771,791,168 params | **19.95%** |
-
-
-## Mining Dataset Statistics
-
-- **Number of texts used for mining**: 200,000 texts  
-- **Dataset**: [Lumberjackk/fineweb-2-trimming](https://huggingface.co/datasets/Lumberjackk/fineweb-2-trimming)
-
-## Usage
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "AlphaEdge-AI/Qwen3.5-2B-bak-32768"
-
-# load the tokenizer and the model
-tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto",
-    trust_remote_code=True
-)
-
-# prepare the model input
-prompt = "Your prompt in Bashkir."
-messages = [
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-# conduct text completion
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=32768
-)
-output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
-content = tokenizer.decode(output_ids, skip_special_tokens=True)
-
-print("content:", content)
-
-```
-
-## Citation
-
-#### Qwen3.5
-
-```bibtex
-@misc{qwen3.5,
-    title  = {Qwen3.5: Towards Native Multimodal Agents},
-    author = {Qwen Team},
-    month  = {February},
-    year   = {2026},
-    url    = {https://qwen.ai/blog?id=qwen3.5}
-}
-```
+---
+pipeline_tag: text-generation
+language: bak
+license: apache-2.0
+tags:
+  - trimmed
+library_name: transformers
+base_model: Qwen3.5-2B
+base_model_relation: quantized
+datasets:
+  - lbourdois/fineweb-2-trimming
+---
+
+# Qwen3.5-2B-bak-32768
+This model is a **19.95% smaller** version of [Qwen/Qwen3.5-2B](https://huggingface.co/Qwen/Qwen3.5-2B) optimized for **Bashkir** language via vocabulary size reduction using the [trimming](https://huggingface.co/blog/lbourdois/introduction-to-trimming) method.  
+This trimmed model should perform similarly to the original model with only 32,768 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.
+
+## Model Statistics
+| Metric | Original | Trimmed | Reduction |
+|--------|----------|---------|-----------|
+| **Vocabulary size** | 248,320 tokens | 32,768 tokens | **86.80%** |
+| **Model size** | 2,213,241,664 params | 1,771,791,168 params | **19.95%** |
+
+![image](https://raw.githubusercontent.com/lbourdois/blog/refs/heads/master/assets/images/Trimming/qwen.5-2B-32768.png)
+
+## Mining Dataset Statistics
+- **Number of texts used for mining**: 179,964 texts  
+- **Dataset**: [lbourdois/fineweb-2-trimming](https://huggingface.co/datasets/lbourdois/fineweb-2-trimming)
+
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "alphaedge-ai/Qwen.5-2B-bak-32768"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# prepare the model input
+prompt = "Your prompt in Bashkir."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=32768
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
+content = tokenizer.decode(output_ids, skip_special_tokens=True)
+
+print("content:", content)
+```
+
+## Citations
+
+#### Qwen3
+```
+@misc{qwen3.5,
+    title  = {Qwen3.5: Towards Native Multimodal Agents},
+    author = {Qwen Team},
+    month  = {February},
+    year   = {2026},
+    url    = {https://qwen.ai/blog?id=qwen3.5}
+}
+```
+
+#### Trimming blog post
+```
+@misc{hf_blogpost_trimming,
+      title={Introduction to Trimming}, 
+      author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},
+      year={2026},
+      url={https://huggingface.co/blog/lbourdois/introduction-to-trimming}, 
+}
+```