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Model: FlameF0X/Qwen2-0.2B-it
Source: Original Platform

README.md

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+---
+library_name: transformers
+base_model:
+- FlameF0X/Qwen2-0.2B-pt
+license: apache-2.0
+datasets:
+- Salesforce/wikitext
+- roneneldan/TinyStories
+- FlameF0X/arXiv-AI-ML
+- Skylion007/openwebtext
+- flytech/python-codes-25k
+- bookcorpus/bookcorpus
+- HuggingFaceH4/ultrachat_200k
+- openai/gsm8k
+- microsoft/orca-math-word-problems-200k
+- laion/OIG
+- microsoft/wiki_qa
+metrics:
+- accuracy
+model-index:
+  - name: FlameF0X/Qwen2-0.2B-it
+    results:
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          id: openai/gsm8k
+          name: GSM8K
+          type: gsm8k
+          config: main
+          split: test
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 2.00
+            verified: false
+        source:
+          name: Local Benchmark
+          url: https://huggingface.co/FlameF0X/Qwen2-0.2B-it
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          id: TIGER-Lab/MMLU-Pro
+          name: MMLU-Pro
+          type: TIGER-Lab/MMLU-Pro
+          config: default
+          split: test
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 4.00
+            verified: false
+        source:
+          name: Local Benchmark
+          url: https://huggingface.co/FlameF0X/Qwen2-0.2B-it
+---
+
+## Evaluation Results
+
+| Benchmark | Score |
+|-----------|-------|
+| GSM8K (test) | 2.00% |
+| MMLU-Pro (test) | 4.00% |
+
+> Results obtained via local evaluation. Given the model size (0.2B parameters), low benchmark scores are expected.
+
+## Model Usage
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_path = "FlameF0X/Qwen2-0.2B-it"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Explain how a transformer model works in one sentence."}
+]
+
+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=128,
+    do_sample=True,
+    temperature=0.7
+)
+
+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]
+print(f"--- Assistant Response ---\n{response}")
+```
+
+## Training Data
+
+This model was instruction-tuned on a mixture of:
+
+- `Salesforce/wikitext` — General text
+- `roneneldan/TinyStories` — Short story generation
+- `FlameF0X/arXiv-AI-ML` — AI/ML research papers
+- `Skylion007/openwebtext` — Web text
+- `flytech/python-codes-25k` — Python code
+- `bookcorpus/bookcorpus` — Books
+- `HuggingFaceH4/ultrachat_200k` — Instruction following
+- `openai/gsm8k` — Math reasoning
+- `microsoft/orca-math-word-problems-200k` — Math word problems
+- `laion/OIG` — Open instruction generalist
+- `microsoft/wiki_qa` — Question answering