Quantization made by Richard Erkhov.

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	Q2_K	2.53GB
Mistral-7B-v0.2-meditron-turkish.IQ3_XS.gguf	IQ3_XS	2.81GB
Mistral-7B-v0.2-meditron-turkish.IQ3_S.gguf	IQ3_S	2.96GB
Mistral-7B-v0.2-meditron-turkish.Q3_K_S.gguf	Q3_K_S	2.95GB
Mistral-7B-v0.2-meditron-turkish.IQ3_M.gguf	IQ3_M	3.06GB
Mistral-7B-v0.2-meditron-turkish.Q3_K.gguf	Q3_K	3.28GB
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	Q3_K_L	3.56GB
Mistral-7B-v0.2-meditron-turkish.IQ4_XS.gguf	IQ4_XS	3.67GB
Mistral-7B-v0.2-meditron-turkish.Q4_0.gguf	Q4_0	3.83GB
Mistral-7B-v0.2-meditron-turkish.IQ4_NL.gguf	IQ4_NL	3.87GB
Mistral-7B-v0.2-meditron-turkish.Q4_K_S.gguf	Q4_K_S	3.86GB
Mistral-7B-v0.2-meditron-turkish.Q4_K.gguf	Q4_K	4.07GB
Mistral-7B-v0.2-meditron-turkish.Q4_K_M.gguf	Q4_K_M	4.07GB
Mistral-7B-v0.2-meditron-turkish.Q4_1.gguf	Q4_1	4.24GB
Mistral-7B-v0.2-meditron-turkish.Q5_0.gguf	Q5_0	4.65GB
Mistral-7B-v0.2-meditron-turkish.Q5_K_S.gguf	Q5_K_S	4.65GB
Mistral-7B-v0.2-meditron-turkish.Q5_K.gguf	Q5_K	4.78GB
Mistral-7B-v0.2-meditron-turkish.Q5_K_M.gguf	Q5_K_M	4.78GB
Mistral-7B-v0.2-meditron-turkish.Q5_1.gguf	Q5_1	5.07GB
Mistral-7B-v0.2-meditron-turkish.Q6_K.gguf	Q6_K	5.53GB
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

Mistral-7B-v0.2-meditron-turkish is a finetuned Mistral Model version using Freeze technique on Turkish Meditron dataset of malhajar/meditron-7b-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
Language(s) (NLP): Turkish,English
Finetuned from model: 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.

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

Detailed results can be found here

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