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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+Mistral-7B-v0.2-meditron-turkish - GGUF
+- Model creator: https://huggingface.co/malhajar/
+- Original model: https://huggingface.co/malhajar/Mistral-7B-v0.2-meditron-turkish/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [Mistral-7B-v0.2-meditron-turkish.Q2_K.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q2_K.gguf) | Q2_K | 2.53GB |
+| [Mistral-7B-v0.2-meditron-turkish.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
+| [Mistral-7B-v0.2-meditron-turkish.IQ3_S.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.IQ3_S.gguf) | IQ3_S | 2.96GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
+| [Mistral-7B-v0.2-meditron-turkish.IQ3_M.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.IQ3_M.gguf) | IQ3_M | 3.06GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q3_K.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q3_K.gguf) | Q3_K | 3.28GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
+| [Mistral-7B-v0.2-meditron-turkish.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q4_0.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q4_0.gguf) | Q4_0 | 3.83GB |
+| [Mistral-7B-v0.2-meditron-turkish.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q4_K.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q4_K.gguf) | Q4_K | 4.07GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q4_1.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q4_1.gguf) | Q4_1 | 4.24GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q5_0.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q5_0.gguf) | Q5_0 | 4.65GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q5_K.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q5_K.gguf) | Q5_K | 4.78GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q5_1.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q5_1.gguf) | Q5_1 | 5.07GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q6_K.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q6_K.gguf) | Q6_K | 5.53GB |
+| [Mistral-7B-v0.2-meditron-turkish.Q8_0.gguf](https://huggingface.co/RichardErkhov/malhajar_-_Mistral-7B-v0.2-meditron-turkish-gguf/blob/main/Mistral-7B-v0.2-meditron-turkish.Q8_0.gguf) | Q8_0 | 7.17GB |
+
+
+
+
+Original model description:
+---
+language:
+- tr
+- en
+license: apache-2.0
+datasets:
+- malhajar/meditron-tr
+model-index:
+- name: Mistral-7B-v0.2-meditron-turkish
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 59.56
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 81.79
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU (5-Shot)
+      type: cais/mmlu
+      config: all
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 60.35
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 66.19
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 76.24
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 35.94
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/Mistral-7B-v0.2-meditron-turkish
+      name: Open LLM Leaderboard
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+Mistral-7B-v0.2-meditron-turkish is a finetuned Mistral Model version using Freeze technique on Turkish Meditron dataset of [`malhajar/meditron-7b-tr`](https://huggingface.co/datasets/malhajar/meditron-tr) using SFT Training.
+This model can answer information about different excplicit ideas in medicine in Turkish and English
+
+### Model Description
+
+- **Finetuned by:** [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/) 
+- **Language(s) (NLP):** Turkish,English
+- **Finetuned from model:** [`mistralai/Mistral-7B-Instruct-v0.2`](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
+
+### Prompt Template For Turkish Generation
+```
+### Kullancı:
+```
+### Prompt Template For English Generation
+```
+### User:
+```
+
+## How to Get Started with the Model
+
+Use the code sample provided in the original post to interact with the model.
+```python
+from transformers import AutoTokenizer,AutoModelForCausalLM
+ 
+model_id = "malhajar/Mistral-7B-v0.2-meditron-turkish"
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
+                                             device_map="auto",
+                                             torch_dtype=torch.float16,
+                                             revision="main")
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+question: "Akciğer kanseri nedir?"
+# For generating a response
+prompt = '''
+### Kullancı:
+{question} 
+'''
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+output = model.generate(inputs=input_ids,max_new_tokens=512,pad_token_id=tokenizer.eos_token_id,top_k=50, do_sample=True,
+        top_p=0.95)
+response = tokenizer.decode(output[0])
+
+print(response)
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_malhajar__Mistral-7B-v0.2-meditron-turkish)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |63.34|
+|AI2 Reasoning Challenge (25-Shot)|59.56|
+|HellaSwag (10-Shot)              |81.79|
+|MMLU (5-Shot)                    |60.35|
+|TruthfulQA (0-shot)              |66.19|
+|Winogrande (5-shot)              |76.24|
+|GSM8k (5-shot)                   |35.94|
+
+
+