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RichardErkhov/
stdevelopment_-_mistral7bIns-dpo-v0rc0-gguf

Name: stdevelopment_-_mistral7bIns-dpo-v0rc0-gguf
Availability: InStock

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mistral7bIns-dpo-v0rc0 - GGUF

Model creator: https://huggingface.co/stdevelopment/
Original model: https://huggingface.co/stdevelopment/mistral7bIns-dpo-v0rc0/

Name	Quant method	Size
mistral7bIns-dpo-v0rc0.Q2_K.gguf	Q2_K	2.54GB
mistral7bIns-dpo-v0rc0.IQ3_XS.gguf	IQ3_XS	2.82GB
mistral7bIns-dpo-v0rc0.IQ3_S.gguf	IQ3_S	2.97GB
mistral7bIns-dpo-v0rc0.Q3_K_S.gguf	Q3_K_S	2.95GB
mistral7bIns-dpo-v0rc0.IQ3_M.gguf	IQ3_M	3.06GB
mistral7bIns-dpo-v0rc0.Q3_K.gguf	Q3_K	3.28GB
mistral7bIns-dpo-v0rc0.Q3_K_M.gguf	Q3_K_M	3.28GB
mistral7bIns-dpo-v0rc0.Q3_K_L.gguf	Q3_K_L	3.56GB
mistral7bIns-dpo-v0rc0.IQ4_XS.gguf	IQ4_XS	3.68GB
mistral7bIns-dpo-v0rc0.Q4_0.gguf	Q4_0	3.83GB
mistral7bIns-dpo-v0rc0.IQ4_NL.gguf	IQ4_NL	3.87GB
mistral7bIns-dpo-v0rc0.Q4_K_S.gguf	Q4_K_S	3.86GB
mistral7bIns-dpo-v0rc0.Q4_K.gguf	Q4_K	4.07GB
mistral7bIns-dpo-v0rc0.Q4_K_M.gguf	Q4_K_M	4.07GB
mistral7bIns-dpo-v0rc0.Q4_1.gguf	Q4_1	4.24GB
mistral7bIns-dpo-v0rc0.Q5_0.gguf	Q5_0	4.66GB
mistral7bIns-dpo-v0rc0.Q5_K_S.gguf	Q5_K_S	4.66GB
mistral7bIns-dpo-v0rc0.Q5_K.gguf	Q5_K	4.78GB
mistral7bIns-dpo-v0rc0.Q5_K_M.gguf	Q5_K_M	4.78GB
mistral7bIns-dpo-v0rc0.Q5_1.gguf	Q5_1	5.07GB
mistral7bIns-dpo-v0rc0.Q6_K.gguf	Q6_K	5.54GB
mistral7bIns-dpo-v0rc0.Q8_0.gguf	Q8_0	7.17GB

Original model description:

tags:

autotrain
text-generation-inference
text-generation
peft library_name: transformers base_model: mistralai/Mistral-7B-Instruct-v0.3 widget:
- messages:
  - role: user content: What is your favorite condiment? license: other

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "Hello! How can I assist you today?"
print(response)

RichardErkhov/stdevelopment_-_mistral7bIns-dpo-v0rc0-gguf

Original model description:

Model Trained Using AutoTrain

Usage

RichardErkhov/
stdevelopment_-_mistral7bIns-dpo-v0rc0-gguf