+
+# MaziyarPanahi/calme-2.1-phi3.5-4b
+
+This model is a fine-tuned version of the `microsoft/Phi-3.5-mini-instruct`, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications.
+
+## Use Cases
+
+This model is suitable for a wide range of applications, including but not limited to:
+
+- Advanced question-answering systems
+- Intelligent chatbots and virtual assistants
+- Content generation and summarization
+- Code generation and analysis
+- Complex problem-solving and decision support
+
+# ⚡ Quantized GGUF
+
+Here are the quants: [calme-2.1-phi3.5-4b-GGUF](https://huggingface.co/MaziyarPanahi/calme-2.1-phi3.5-4b-GGUF)
+
+
+# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+
+Coming soon!
+
+# Prompt Template
+
+This model uses `ChatML` prompt template:
+
```
+<|system|>
+You are a helpful assistant.<|end|>
+<|user|>
+How to explain Internet for a medieval knight?<|end|>
+<|assistant|>
+````
+
+# How to use
+
+
```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('QuantFactory/calme-2.1-phi3.5-4b-GGUF')
-```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/QuantFactory/calme-2.1-phi3.5-4b-GGUF.git
+
+# Use a pipeline as a high-level helper
+
+from transformers import pipeline
+
+messages = [
+ {"role": "user", "content": "Who are you?"},
+]
+pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.1-phi3.5-4b")
+pipe(messages)
+
+
+# Load model directly
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-phi3.5-4b")
+model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-phi3.5-4b")
```
-如果您是本模型的贡献者，我们邀请您根据模型贡献文档，及时完善模型卡片内容。
\ No newline at end of file + +# Ethical Considerations + +As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments. +# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) +Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__calme-2.1-phi3.5-4b) + +| Metric |Value| +|-------------------|----:| +|Avg. |27.01| +|IFEval (0-Shot) |56.59| +|BBH (3-Shot) |36.11| +|MATH Lvl 5 (4-Shot)|14.43| +|GPQA (0-shot) |12.53| +|MuSR (0-shot) | 9.77| +|MMLU-PRO (5-shot) |32.61| + + diff --git a/calme-2.1-phi3.5-4b.Q2_K.gguf b/calme-2.1-phi3.5-4b.Q2_K.gguf new file mode 100644 index 0000000..6a0e4a3 --- /dev/null +++ b/calme-2.1-phi3.5-4b.Q2_K.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f7fbcf42b962f477d3403c5cf5376de6c4ef106268b908243a3097aabd29750 +size 1416204672 diff --git a/calme-2.1-phi3.5-4b.Q3_K_L.gguf b/calme-2.1-phi3.5-4b.Q3_K_L.gguf 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